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keywords = {leibnizailab},
title = {RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image Generation},
year = 2025
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%1 ReiBer2025a
%A Reinders, Christoph
%A Berdan, Radu
%A Besbinar, Beril
%A Otsuka, Junji
%A Iso, Daisuke
%B Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
%D 2025
%T RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image Generation - Wehrbein, T., Rudolph, M., Rosenhahn, B., and Wandt, B. (2025)Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
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keywords = {detectors},
month = {02},
title = {Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery},
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%1 WehRud2025
%A Wehrbein, Tom
%A Rudolph, Marco
%A Rosenhahn, Bodo
%A Wandt, Bastian
%B IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
%D 2025
%T Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery
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number = 6,
pages = {147-150},
title = {AI for Speedrunning},
volume = 14,
year = 2024
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%A Carnovalini, Filippo
%A Charity, M
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%J Computational Creativity for Game Development (Dagstuhl Seminar 24261)
%N 6
%P 147-150
%R 10.4230/DagRep.14.6.130
%T AI for Speedrunning
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keywords = {Flow-guided},
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title = {FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing},
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%D 2024
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%J 15th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m68079
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pages = {1–2},
title = {AbstractSwarm Multi-Agent Logistics Competition: Multi-Agent Collaboration for Improving A Priori Unknown Logistics Scenarios},
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%A Apeldoorn, Daan
%A Dockhorn, Alexander
%A Panholzer, Torsten
%B Proceedings of the Genetic and Evolutionary Computation Conference Companion
%D 2024
%P 1–2
%R 10.1145/3638530.3664053
%T AbstractSwarm Multi-Agent Logistics Competition: Multi-Agent Collaboration for Improving A Priori Unknown Logistics Scenarios
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%1 KluRos2024a
%A Kluger, Florian
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%B AAAI
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%T PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus - Hinrichs, R., Gerkens, K., Lange, A., and Ostermann, J. (2024)Blind Extraction of Guitar Effects Through Blind System Inversion and Neural Guitar Effect Modeling, EURASIP Journal on Audio, Speech, and Music Processing.
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%A Hinrichs, Reemt
%A Gerkens, Kevin
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%J EURASIP Journal on Audio, Speech, and Music Processing
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%J preprint
%T Quantum Normalizing Flows for Anomaly Detection - Chen, S., Xu, M., Ren, J., Cong, Y., He, S., Xie, Y., Sinha, A., Luo, P., Xiang, T., and Perez-Rua, J.-M. (2024)GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation. In Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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booktitle = {Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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title = {GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation},
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%B Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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%T GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation - Wallat, J., Jatowt, A., and Anand, A. (2024)Temporal Blind Spots in Large Language Models, ACM International Conference on Web Search and Data Mining (WSDM) (Proceeding, no out yet, Ed.) 17.
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title = {Temporal Blind Spots in Large Language Models},
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%A Wallat, Jonas
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%T Temporal Blind Spots in Large Language Models
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%T Quantum Doeblin coefficients: A simple upper bound on contraction coefficients - Chen, Y.-H., Gao, Z.-L., Benjak, M., and Peng, W.-H. (2024)Response to Call for Learning-Based Video Codecs for Study of Quality Assessment by NYCU and LUH, 14th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m66163.
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%A Reinders, Christoph
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%J Volunteered Geographic Information
%T Two Worlds in One Network: Fusing Deep Learning and Random Forests for Classification and Object Detection - Schubert, F., Mahlau, Y., Bethmann, K., Hartmann, F., Caspary, R., Munderloh, M., Ostermann, J., and Rosenhahn, B. (2024)Quantized Inverse Design for Photonic Integrated Circuits, Preprint.
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title = {Quantized Inverse Design for Photonic Integrated Circuits},
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%1 SchMah2024
%A Schubert, Frederik
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%A Bethmann, Konrad
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%T Quantized Inverse Design for Photonic Integrated Circuits - Hinrichs, R. (2024)Kompression der Erregungsmuster von Cochlea-Implantaten, VDI Verlag.
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}%0 Book
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%A Hinrichs, Reemt
%D 2024
%I VDI Verlag
%T Kompression der Erregungsmuster von Cochlea-Implantaten - M{ü}ntefering, F., Adhisantoso, Y. G., Chandak, S., Ostermann, J., Hernaez, M., and Voges, J. (2024)Genie: the first open-source ISO/IEC encoder for genomic data, Communications Biology 7.
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%N 553
%R 10.1038/s42003-024-06249-8
%T Genie: the first open-source ISO/IEC encoder for genomic data
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title = {Q-SENN: Quantized Self-Explaining Neural Networks},
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%B AAAI Technical Track on Safe, Robust and Responsible AI
%D 2024
%N 19
%P 21482--21491
%R 10.1609/AAAI.V38I19.30145
%T Q-SENN: Quantized Self-Explaining Neural Networks
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title = {Personalized Dynamic Difficulty Adjustment - Imitation Learning Meets Reinforcement Learning},
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%A Fuchs, Ronja
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%A Dockhorn, Alexander
%B Proceedings of the IEEE Conference on Games 2024
%D 2024
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%T Personalized Dynamic Difficulty Adjustment - Imitation Learning Meets Reinforcement Learning - Kaiser, T., Vladimir, U., and Rosenhahn, B. (2024)CHOTA: A Higher Order Accuracy Metric for Cell Tracking. In European Conference on Computer Vision Workshops (ECCVW).
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title = {CHOTA: A Higher Order Accuracy Metric for Cell Tracking},
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%A Kaiser, Timo
%A Vladimir, Ulman
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%D 2024
%T CHOTA: A Higher Order Accuracy Metric for Cell Tracking - Adhisantoso, Y. G., and Cheung, P. (2024)Input to Requirements about MPEG-G Profiles - m68659, ISO/IEC JTC 1/SC 29/WG 8.
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%T Input to Requirements about MPEG-G Profiles - m68659 - Adhisantoso, Y. G., Cheung, P., {Ö}zt{ü}rk, {Ü}nsal, Hernaez, M., Krasinski, R., M{ü}ntefering, F., and Voges, J. (2024)Recommendations of the AHG on MPEG-G Profiles - m68661, ISO/IEC JTC 1/SC 29/WG 8.
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month = {07},
title = {Recommendations of the AHG on MPEG-G Profiles - m68661},
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}%0 Journal Article
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%A M{ü}ntefering, Fabian
%A Voges, Jan
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%J ISO/IEC JTC 1/SC 29/WG 8
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title = {Bitstream Generation and Bit Rate Fitting Results of MaskCRT for CVQM UHD and 4K Sequences},
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%A Bundies, Gabriel L.
%A Meyer-Bockenkamp, Fiona
%A Bleich, Stefan
%A Pathak, Hansi
%A Ziert, Yvonne
%A Neuhaus, Barbara
%A M{ü}ller, Franz-Josef
%A Pollmann, Iris
%A Illig, Thomas
%A M{ü}cke, Stefanie
%A M{ü}ller, Meike
%A M{ö}ller, Brinja Kira
%A Oeltze-Jafra, Steffen
%A Kacprowski, Tim
%A Voges, Jan
%A M{ü}ntefering, Fabian
%A Scheiber, Josef
%A Reif, Andreas
%A Aichholzer, Mareike
%A Reif-Leonhard, Christine
%A Schmidt-Kassow, Maren
%A Hegerl, Ulrich
%A Reich, Hanna
%A Unterecker, Stefan
%A Weber, Heike
%A Deckert, J{ü}rgen
%A B{ö}ssel-Debbert, Nicole
%A Grabe, Hans J.
%A Lucht, Michael
%A Frieling, Helge
%D 2024
%J Trials
%N 247
%R 10.1186/s13063-024-08061-5
%T Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients—a randomized rater-blinded trial
%V 25 - Lange, A., Xu, R., K{ä}ding, M., Marx, S., and Ostermann, J. (2024)Matched Filter for Acoustic Emission Monitoring in Noisy Environments: Application to Wire Break Detection (accepted), acoustics, Special Issue: Advances in Industrial and Research Applications of Acoustic Emission Testing.
@article{AleRon2024a,
author = {Lange, Alexander and Xu, Ronghua and K{ä}ding, Max and Marx, Steffen and Ostermann, J{ö}rn},
journal = {acoustics, Special Issue: Advances in Industrial and Research Applications of Acoustic Emission Testing},
keywords = {Emission},
title = {Matched Filter for Acoustic Emission Monitoring in Noisy Environments: Application to Wire Break Detection (accepted)},
year = 2024
}%0 Journal Article
%1 AleRon2024a
%A Lange, Alexander
%A Xu, Ronghua
%A K{ä}ding, Max
%A Marx, Steffen
%A Ostermann, J{ö}rn
%D 2024
%J acoustics, Special Issue: Advances in Industrial and Research Applications of Acoustic Emission Testing
%T Matched Filter for Acoustic Emission Monitoring in Noisy Environments: Application to Wire Break Detection (accepted) - Xuan, Q. L., Munderloh, M., and Ostermann, J. (2024)Self-supervised Domain Adaptation for Machinery Remaining Useful Life Prediction (accepted), Journal on Reliability Engineering and System Safety, Special Issue: RUL Prediction and System Reliability of Complex Systems.
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author = {Xuan, Quy Le and Munderloh, Marco and Ostermann, J{ö}rn},
journal = {Journal on Reliability Engineering and System Safety, Special Issue: RUL Prediction and System Reliability of Complex Systems},
keywords = {Adaptation},
title = {Self-supervised Domain Adaptation for Machinery Remaining Useful Life Prediction (accepted)},
year = 2024
}%0 Journal Article
%1 LeXMun2024
%A Xuan, Quy Le
%A Munderloh, Marco
%A Ostermann, J{ö}rn
%D 2024
%J Journal on Reliability Engineering and System Safety, Special Issue: RUL Prediction and System Reliability of Complex Systems
%T Self-supervised Domain Adaptation for Machinery Remaining Useful Life Prediction (accepted) - Cong, Y., Xu, M., Simon, C., Chen, S., Ren, J., Xie, Y., Perez-Rua, J.-M., Rosenhahn, B., Xiang, T., and He, S. (2024)FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing. In International Conference on Learning Representations (ICLR).
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author = {Cong, Yuren and Xu, Mengmeng and Simon, Christian and Chen, Shoufa and Ren, Jiawei and Xie, Yanping and Perez-Rua, Juan-Manuel and Rosenhahn, Bodo and Xiang, Tao and He, Sen},
booktitle = {International Conference on Learning Representations (ICLR)},
keywords = {from:tntl3s},
title = {FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing},
year = 2024
}%0 Conference Paper
%1 ConXu2024a
%A Cong, Yuren
%A Xu, Mengmeng
%A Simon, Christian
%A Chen, Shoufa
%A Ren, Jiawei
%A Xie, Yanping
%A Perez-Rua, Juan-Manuel
%A Rosenhahn, Bodo
%A Xiang, Tao
%A He, Sen
%B International Conference on Learning Representations (ICLR)
%D 2024
%T FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing - Hinrichs, R., and Ostermann, J. (2024)Pruning-aware Loss Functions for STOI-Optimized Pruned Recurrent Autoencoders for the Compression of the Stimulation Patterns of Cochlear Implants at Zero Delay. In Asilomar Conference on Signals, Systems, and Computers.
@inproceedings{HinOst2024,
author = {Hinrichs, Reemt and Ostermann, J{ö}rn},
booktitle = {Asilomar Conference on Signals, Systems, and Computers},
keywords = {leibnizailab},
month = 10,
title = {Pruning-aware Loss Functions for STOI-Optimized Pruned Recurrent Autoencoders for the Compression of the Stimulation Patterns of Cochlear Implants at Zero Delay},
year = 2024
}%0 Conference Paper
%1 HinOst2024
%A Hinrichs, Reemt
%A Ostermann, J{ö}rn
%B Asilomar Conference on Signals, Systems, and Computers
%D 2024
%T Pruning-aware Loss Functions for STOI-Optimized Pruned Recurrent Autoencoders for the Compression of the Stimulation Patterns of Cochlear Implants at Zero Delay - Tang, M., Antić, Željko, Fardzadeh, P., Pietzsch, S., Schröder, C., Eberhardt, A., van Bömmel, A., Escherich, G., Hofmann, W., Horstmann, M. A., Illig, T., McCrary, J. M., Lentes, J., Metzler, M., Nejdl, W., Schlegelberger, B., Schrappe, M., Zimmermann, M., Miarka-Walczyk, K., Patsorczak, A., Cario, G., Renard, B. Y., Stanulla, M., and Bergmann, A. K. (2024)An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia, eBioMedicine, Elsevier BV 104, 105171.
@article{Tang_2024,
author = {Tang, Ming and Antić, Željko and Fardzadeh, Pedram and Pietzsch, Stefan and Schröder, Charlotte and Eberhardt, Adrian and van Bömmel, Alena and Escherich, Gabriele and Hofmann, Winfried and Horstmann, Martin A. and Illig, Thomas and McCrary, J. Matt and Lentes, Jana and Metzler, Markus and Nejdl, Wolfgang and Schlegelberger, Brigitte and Schrappe, Martin and Zimmermann, Martin and Miarka-Walczyk, Karolina and Patsorczak, Agata and Cario, Gunnar and Renard, Bernhard Y. and Stanulla, Martin and Bergmann, Anke Katharina},
journal = {eBioMedicine},
keywords = {l3s},
month = {06},
pages = 105171,
publisher = {Elsevier BV},
title = {An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia},
volume = 104,
year = 2024
}%0 Journal Article
%1 Tang_2024
%A Tang, Ming
%A Antić, Željko
%A Fardzadeh, Pedram
%A Pietzsch, Stefan
%A Schröder, Charlotte
%A Eberhardt, Adrian
%A van Bömmel, Alena
%A Escherich, Gabriele
%A Hofmann, Winfried
%A Horstmann, Martin A.
%A Illig, Thomas
%A McCrary, J. Matt
%A Lentes, Jana
%A Metzler, Markus
%A Nejdl, Wolfgang
%A Schlegelberger, Brigitte
%A Schrappe, Martin
%A Zimmermann, Martin
%A Miarka-Walczyk, Karolina
%A Patsorczak, Agata
%A Cario, Gunnar
%A Renard, Bernhard Y.
%A Stanulla, Martin
%A Bergmann, Anke Katharina
%D 2024
%I Elsevier BV
%J eBioMedicine
%P 105171
%R 10.1016/j.ebiom.2024.105171
%T An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia
%U http://dx.doi.org/10.1016/j.ebiom.2024.105171
%V 104 - Liu, B., Rosenhahn, B., Illig, T., and DeLuca, D. S. (2024)A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data, PLOS Computational Biology 20, 1–22.
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author = {Liu, Bin and Rosenhahn, Bodo and Illig, Thomas and DeLuca, David S.},
journal = {PLOS Computational Biology},
keywords = {leibnizailab},
month = {07},
number = 7,
pages = {1-22},
title = {A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data},
volume = 20,
year = 2024
}%0 Journal Article
%1 LiuRos2024a
%A Liu, Bin
%A Rosenhahn, Bodo
%A Illig, Thomas
%A DeLuca, David S.
%D 2024
%J PLOS Computational Biology
%N 7
%P 1-22
%R 10.1371/journal.pcbi.1011198
%T A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data
%V 20 - Oguz, M. K., and Dockhorn, A. (2024)Markov Senior - Learning Markov Junior Grammars to Generate User-specified Content. In Proceedings of the IEEE Conference on Games 2024, pp. 1–8.
@inproceedings{OguDoc2024,
author = {Oguz, Mehmet Kayra and Dockhorn, Alexander},
booktitle = {Proceedings of the IEEE Conference on Games 2024},
keywords = {leibnizailab},
pages = {1-8},
title = {Markov Senior - Learning Markov Junior Grammars to Generate User-specified Content},
year = 2024
}%0 Conference Paper
%1 OguDoc2024
%A Oguz, Mehmet Kayra
%A Dockhorn, Alexander
%B Proceedings of the IEEE Conference on Games 2024
%D 2024
%P 1-8
%T Markov Senior - Learning Markov Junior Grammars to Generate User-specified Content - Jiwatode, M., Schlecht, L., and Dockhorn, A. (2024)Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization. In Proceedings of the Conference on Games 2024, pp. 1–8.
@inproceedings{JiwSch2024,
author = {Jiwatode, Mohit and Schlecht, Leon and Dockhorn, Alexander},
booktitle = {Proceedings of the Conference on Games 2024},
keywords = {Schedules},
pages = {1-8},
title = {Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization},
year = 2024
}%0 Conference Paper
%1 JiwSch2024
%A Jiwatode, Mohit
%A Schlecht, Leon
%A Dockhorn, Alexander
%B Proceedings of the Conference on Games 2024
%D 2024
%P 1-8
%T Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization - Benjak, M., and Ostermann, J. (2024)Comparison of VVC and LCEVC with a wide set of configurations for 4K content, 16th Meeting of ISO/IEC JTC 1/SC 29/WG 4 Document m68908.
@article{BenOst2024a,
author = {Benjak, Martin and Ostermann, J{ö}rn},
journal = {16th Meeting of ISO/IEC JTC 1/SC 29/WG 4 Document m68908},
keywords = {Comparison},
month = {07},
title = {Comparison of VVC and LCEVC with a wide set of configurations for 4K content},
year = 2024
}%0 Journal Article
%1 BenOst2024a
%A Benjak, Martin
%A Ostermann, J{ö}rn
%D 2024
%J 16th Meeting of ISO/IEC JTC 1/SC 29/WG 4 Document m68908
%T Comparison of VVC and LCEVC with a wide set of configurations for 4K content - Mahlau, Y., Schubert, F., and Rosenhahn, B. (2024)Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games. In Proceedings of the 41st International Conference on Machine Learning (ICML).
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author = {Mahlau, Yannik and Schubert, Frederik and Rosenhahn, Bodo},
booktitle = {Proceedings of the 41st International Conference on Machine Learning (ICML)},
keywords = {Zero-Shot},
month = {07},
title = {Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games},
year = 2024
}%0 Conference Paper
%1 MahSch2024a
%A Mahlau, Yannik
%A Schubert, Frederik
%A Rosenhahn, Bodo
%B Proceedings of the 41st International Conference on Machine Learning (ICML)
%D 2024
%T Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games - Dockhorn, A., Eberhardinger, M., Hu, C., and M{ü}ller-Brockhausen, M. (2024)Skill-Discovery in (Strategy) Games, Computational Creativity for Game Development (Dagstuhl Seminar 24261) 14, 150–154.
@article{DocEbe2024a,
author = {Dockhorn, Alexander and Eberhardinger, Manuel and Hu, Chengpeng and M{ü}ller-Brockhausen, Matthias},
journal = {Computational Creativity for Game Development (Dagstuhl Seminar 24261)},
keywords = {leibnizailab},
month = 12,
number = 6,
pages = {150-154},
title = {Skill-Discovery in (Strategy) Games},
volume = 14,
year = 2024
}%0 Journal Article
%1 DocEbe2024a
%A Dockhorn, Alexander
%A Eberhardinger, Manuel
%A Hu, Chengpeng
%A M{ü}ller-Brockhausen, Matthias
%D 2024
%J Computational Creativity for Game Development (Dagstuhl Seminar 24261)
%N 6
%P 150-154
%R 10.4230/DagRep.14.6.130
%T Skill-Discovery in (Strategy) Games
%V 14 - Chen, Y.-H., Chen, C.-W., Gao, Z.-L., Benjak, M., and Peng, W.-H. (2024)Results on the Bit Rate Fitting for MaskCRT, 15th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m67455.
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author = {Chen, Yi-Hsin and Chen, Cheng-Wei and Gao, Zong-Lin and Benjak, Martin and Peng, Wen-Hsiao},
journal = {15th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m67455},
keywords = {MaskCRT},
month = {04},
title = {Results on the Bit Rate Fitting for MaskCRT},
year = 2024
}%0 Journal Article
%1 CheChe2024b
%A Chen, Yi-Hsin
%A Chen, Cheng-Wei
%A Gao, Zong-Lin
%A Benjak, Martin
%A Peng, Wen-Hsiao
%D 2024
%J 15th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m67455
%T Results on the Bit Rate Fitting for MaskCRT - Xu, R., Beltran-Gutierrez, R. E., K{ä}ding, M., Lange, A., Marx, S., and Ostermann, J. (2024)Frequency dependent amplitude response of different couplant materials for mounting piezoelectric sensors, NDT \& E International 141.
@article{RonRau2024,
author = {Xu, Ronghua and Beltran-Gutierrez, Raul Enrique and K{ä}ding, Max and Lange, Alexander and Marx, Steffen and Ostermann, J{ö}rn},
journal = {NDT \& E International},
keywords = {piezoelectric},
month = {01},
title = {Frequency dependent amplitude response of different couplant materials for mounting piezoelectric sensors},
volume = 141,
year = 2024
}%0 Journal Article
%1 RonRau2024
%A Xu, Ronghua
%A Beltran-Gutierrez, Raul Enrique
%A K{ä}ding, Max
%A Lange, Alexander
%A Marx, Steffen
%A Ostermann, J{ö}rn
%D 2024
%J NDT \& E International
%T Frequency dependent amplitude response of different couplant materials for mounting piezoelectric sensors
%V 141 - Rudolph, M. S., Lerch, S., Thanasilp, S., Kiss, O., Shaya, O., Vallecorsa, S., Grossi, M., and Holmes, Z. (2024)Trainability barriers and opportunities in quantum generative modeling, npj Quantum Information 10.
@article{RudLer2024,
author = {Rudolph, Manuel S. and Lerch, Sacha and Thanasilp, Supanut and Kiss, Oriel and Shaya, Oxana and Vallecorsa, Sofia and Grossi, Michele and Holmes, Zoe},
journal = {npj Quantum Information},
keywords = {generative},
month = 11,
number = 116,
title = {Trainability barriers and opportunities in quantum generative modeling},
volume = 10,
year = 2024
}%0 Journal Article
%1 RudLer2024
%A Rudolph, Manuel S.
%A Lerch, Sacha
%A Thanasilp, Supanut
%A Kiss, Oriel
%A Shaya, Oxana
%A Vallecorsa, Sofia
%A Grossi, Michele
%A Holmes, Zoe
%D 2024
%J npj Quantum Information
%N 116
%R 10.1038/s41534-024-00902-0
%T Trainability barriers and opportunities in quantum generative modeling
%V 10 - Eberhardinger, M., Cakmak, D., Dockhorn, A., Gaina, R., Goodman, J., Hoover, A. K., Lucas, S. M., Maghsudi, S., and Liebana, D. P. (2024)LLM-based Program Search for Games, Computational Creativity for Game Development (Dagstuhl Seminar 24261) 14, 156–166.
@article{EbeCak2024,
author = {Eberhardinger, Manuel and Cakmak, Duygu and Dockhorn, Alexander and Gaina, Raluca and Goodman, James and Hoover, Amy K. and Lucas, Simon M. and Maghsudi, Setareh and Liebana, Diego Perez},
journal = {Computational Creativity for Game Development (Dagstuhl Seminar 24261)},
keywords = {for},
month = 12,
number = 6,
pages = {156-166},
title = {LLM-based Program Search for Games},
volume = 14,
year = 2024
}%0 Journal Article
%1 EbeCak2024
%A Eberhardinger, Manuel
%A Cakmak, Duygu
%A Dockhorn, Alexander
%A Gaina, Raluca
%A Goodman, James
%A Hoover, Amy K.
%A Lucas, Simon M.
%A Maghsudi, Setareh
%A Liebana, Diego Perez
%D 2024
%J Computational Creativity for Game Development (Dagstuhl Seminar 24261)
%N 6
%P 156-166
%R 10.4230/DagRep.14.6.130
%T LLM-based Program Search for Games
%V 14 - Maharlou, H., B{ö}ssel-Debbert, N., Lucht, M., Maier, H. B., M{ü}cke, S., M{ü}ntefering, F., Neuhaus, B., Prokein, J., Reif-Leonhard, C., Voges, J., Weber, H., Weihs, A., Frieling, H., and Oeltze-Jafra, S. (2024)Cooperative Design of a Dashboard for Monitoring the P4D Cohort Study on Major Depression. In EuroVisPosters2024.
@inproceedings{MahBoe2024a,
author = {Maharlou, Hamidreza and B{ö}ssel-Debbert, Nicole and Lucht, Michael and Maier, Hannah B. and M{ü}cke, Stefanie and M{ü}ntefering, Fabian and Neuhaus, Barbara and Prokein, Jana and Reif-Leonhard, Christine and Voges, Jan and Weber, Heike and Weihs, Antoine and Frieling, Helge and Oeltze-Jafra, Steffen},
booktitle = {EuroVisPosters2024},
keywords = {Dashboard},
month = {05},
title = {Cooperative Design of a Dashboard for Monitoring the P4D Cohort Study on Major Depression},
year = 2024
}%0 Conference Paper
%1 MahBoe2024a
%A Maharlou, Hamidreza
%A B{ö}ssel-Debbert, Nicole
%A Lucht, Michael
%A Maier, Hannah B.
%A M{ü}cke, Stefanie
%A M{ü}ntefering, Fabian
%A Neuhaus, Barbara
%A Prokein, Jana
%A Reif-Leonhard, Christine
%A Voges, Jan
%A Weber, Heike
%A Weihs, Antoine
%A Frieling, Helge
%A Oeltze-Jafra, Steffen
%B EuroVisPosters2024
%D 2024
%R 10.2312/evp.20241081
%T Cooperative Design of a Dashboard for Monitoring the P4D Cohort Study on Major Depression - Adhisantoso, Y. G., K{ö}rner, T., M{ü}ntefering, F., Ohm, O., and Voges, J. (2024)HiCMC: High-Efficiency Contact Matrix Compressor (accepted). In BMC Bioinformatics.
@inproceedings{AdhKoe2024,
author = {Adhisantoso, Yeremia Gunawan and K{ö}rner, Tim and M{ü}ntefering, Fabian and Ohm, Ostermann and Voges, Jan},
booktitle = {BMC Bioinformatics},
keywords = {HICMC},
month = {08},
title = {HiCMC: High-Efficiency Contact Matrix Compressor (accepted)},
year = 2024
}%0 Conference Paper
%1 AdhKoe2024
%A Adhisantoso, Yeremia Gunawan
%A K{ö}rner, Tim
%A M{ü}ntefering, Fabian
%A Ohm, Ostermann
%A Voges, Jan
%B BMC Bioinformatics
%D 2024
%T HiCMC: High-Efficiency Contact Matrix Compressor (accepted) - Chen, Y.-H., Ho, K.-W., Benjak, M., Ostermann, J., and Peng, W.-H. (2024)On the Rate-Distortion-Complexity Trade-offs of Neural Video Coding. In IEEE 26th International Workshop on Multimedia Signal Processing (MMSP).
@inproceedings{CheHo2024,
author = {Chen, Yi-Hsin and Ho, Kuan-Wei and Benjak, Martin and Ostermann, J{ö}rn and Peng, Wen-Hsiao},
booktitle = {IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)},
keywords = {leibnizailab},
title = {On the Rate-Distortion-Complexity Trade-offs of Neural Video Coding},
year = 2024
}%0 Conference Paper
%1 CheHo2024
%A Chen, Yi-Hsin
%A Ho, Kuan-Wei
%A Benjak, Martin
%A Ostermann, J{ö}rn
%A Peng, Wen-Hsiao
%B IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)
%D 2024
%T On the Rate-Distortion-Complexity Trade-offs of Neural Video Coding - Adhisantoso, Y. G., Cheung, P., {Ö}zt{ü}rk, {Ü}nsal, Hernaez, M., Krasinski, R., M{ü}ntefering, F., and Voges, J. (2024)Recommendations of the AHG on MPEG-G Profiles - m67350, ISO/IEC JTC 1/SC 29/WG 8.
@article{AdhChe2024b,
author = {Adhisantoso, Yeremia Gunawan and Cheung, Patrick and {Ö}zt{ü}rk, {Ü}nsal and Hernaez, Mikel and Krasinski, Ray and M{ü}ntefering, Fabian and Voges, Jan},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {AHG},
month = {04},
title = {Recommendations of the AHG on MPEG-G Profiles - m67350},
year = 2024
}%0 Journal Article
%1 AdhChe2024b
%A Adhisantoso, Yeremia Gunawan
%A Cheung, Patrick
%A {Ö}zt{ü}rk, {Ü}nsal
%A Hernaez, Mikel
%A Krasinski, Ray
%A M{ü}ntefering, Fabian
%A Voges, Jan
%D 2024
%J ISO/IEC JTC 1/SC 29/WG 8
%T Recommendations of the AHG on MPEG-G Profiles - m67350 - Xu, L., Liu, Z., Dockhorn, A., Perez-Liebana, D., Wang, J., Song, L., and Bian, J. (2024)Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning. In Proceedings of the IEEE Conference on Games 2024, pp. 1–8.
@inproceedings{XuLiu2024,
author = {Xu, Linjie and Liu, Zichuan and Dockhorn, Alexander and Perez-Liebana, Diego and Wang, Jinyu and Song, Lei and Bian, Jiang},
booktitle = {Proceedings of the IEEE Conference on Games 2024},
keywords = {Reinforcement},
pages = {1-8},
title = {Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning},
year = 2024
}%0 Conference Paper
%1 XuLiu2024
%A Xu, Linjie
%A Liu, Zichuan
%A Dockhorn, Alexander
%A Perez-Liebana, Diego
%A Wang, Jinyu
%A Song, Lei
%A Bian, Jiang
%B Proceedings of the IEEE Conference on Games 2024
%D 2024
%P 1-8
%T Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning - N{ü}bel, C., Dockhorn, A., and Mostaghim, S. (2024)Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies. In Proceedings of the Conference on Games 2024, pp. 1–4.
@inproceedings{NueDoc2024,
author = {N{ü}bel, Carlo and Dockhorn, Alexander and Mostaghim, Sanaz},
booktitle = {Proceedings of the Conference on Games 2024},
keywords = {Match},
pages = {1-4},
title = {Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies},
year = 2024
}%0 Conference Paper
%1 NueDoc2024
%A N{ü}bel, Carlo
%A Dockhorn, Alexander
%A Mostaghim, Sanaz
%B Proceedings of the Conference on Games 2024
%D 2024
%P 1-4
%T Match Point AI: A Novel AI Framework for Evaluating Data-Driven Tennis Strategies - Xu, L., Perez-Liebana, D., and Dockhorn, A. (2024)Strategy Game-Playing with Size-Constrained State Abstraction. In Proceedings of the IEEE Conference on Games 2024, pp. 1–8.
@inproceedings{XuPer2024,
author = {Xu, Linjie and Perez-Liebana, Diego and Dockhorn, Alexander},
booktitle = {Proceedings of the IEEE Conference on Games 2024},
keywords = {Gäme-Playing},
pages = {1-8},
title = {Strategy Game-Playing with Size-Constrained State Abstraction},
year = 2024
}%0 Conference Paper
%1 XuPer2024
%A Xu, Linjie
%A Perez-Liebana, Diego
%A Dockhorn, Alexander
%B Proceedings of the IEEE Conference on Games 2024
%D 2024
%P 1-8
%T Strategy Game-Playing with Size-Constrained State Abstraction - Gottwald, T., Schier, M., and Rosenhahn, B. (2024)Safe Resetless Reinforcement Learning: Enhancing Training Autonomy with Risk-Averse Agents. In European Conference on Computer Vision Workshops (ECCVW).
@inproceedings{GotSch2024,
author = {Gottwald, Tristan and Schier, Maximilian and Rosenhahn, Bodo},
booktitle = {European Conference on Computer Vision Workshops (ECCVW)},
keywords = {reinforcement},
month = 10,
title = {Safe Resetless Reinforcement Learning: Enhancing Training Autonomy with Risk-Averse Agents},
year = 2024
}%0 Conference Paper
%1 GotSch2024
%A Gottwald, Tristan
%A Schier, Maximilian
%A Rosenhahn, Bodo
%B European Conference on Computer Vision Workshops (ECCVW)
%D 2024
%T Safe Resetless Reinforcement Learning: Enhancing Training Autonomy with Risk-Averse Agents - Benjak, M., and Ostermann, J. (2024)Comparison between LCEVC and VVC, 15th Meeting of ISO/IEC JTC 1/SC 29/WG 4 Document m67212.
@article{BenOst2024,
author = {Benjak, Martin and Ostermann, J{ö}rn},
journal = {15th Meeting of ISO/IEC JTC 1/SC 29/WG 4 Document m67212},
keywords = {Comparison},
month = {04},
title = {Comparison between LCEVC and VVC},
year = 2024
}%0 Journal Article
%1 BenOst2024
%A Benjak, Martin
%A Ostermann, J{ö}rn
%D 2024
%J 15th Meeting of ISO/IEC JTC 1/SC 29/WG 4 Document m67212
%T Comparison between LCEVC and VVC - Kruse, M., Rudolph, M., Woiwode, D., and Rosenhahn, B. (2024)SplatPose \& Detect: Pose-Agnostic 3D Anomaly Detection. In Computer Vision and Pattern Recognition Workshops (CVPRW).
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author = {Kruse, Mathis and Rudolph, Marco and Woiwode, Dominik and Rosenhahn, Bodo},
booktitle = {Computer Vision and Pattern Recognition Workshops (CVPRW)},
keywords = {leibnizailab},
month = {06},
title = {SplatPose \& Detect: Pose-Agnostic 3D Anomaly Detection},
year = 2024
}%0 Conference Paper
%1 KruRud2024
%A Kruse, Mathis
%A Rudolph, Marco
%A Woiwode, Dominik
%A Rosenhahn, Bodo
%B Computer Vision and Pattern Recognition Workshops (CVPRW)
%D 2024
%T SplatPose \& Detect: Pose-Agnostic 3D Anomaly Detection - Adhisantoso, Y. G., and Cheung, P. (2024)Study on the Verification and Enhancement of the MPEG-G Part 6 Specification - m67351, ISO/IEC JTC 1/SC 29/WG 8.
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author = {Adhisantoso, Yeremia Gunawan and Cheung, Patrick},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {Enhancement},
month = {04},
title = {Study on the Verification and Enhancement of the MPEG-G Part 6 Specification - m67351},
year = 2024
}%0 Journal Article
%1 AdhChe2024c
%A Adhisantoso, Yeremia Gunawan
%A Cheung, Patrick
%D 2024
%J ISO/IEC JTC 1/SC 29/WG 8
%T Study on the Verification and Enhancement of the MPEG-G Part 6 Specification - m67351 - Chen, Y.-H., Xie, H.-S., Chen, C.-W., Gao, Z.-L., Benjak, M., Peng, W.-H., and Ostermann, J. (2024)Maskcrt: Masked conditional residual transformer for learned video compression, IEEE Transactions on Circuits and Systems for Video Technology.
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author = {Chen, Yi-Hsin and Xie, Hong-Sheng and Chen, Cheng-Wei and Gao, Zong-Lin and Benjak, Martin and Peng, Wen-Hsiao and Ostermann, J{ö}rn},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
keywords = {Maskcrt},
title = {Maskcrt: Masked conditional residual transformer for learned video compression},
year = 2024
}%0 Journal Article
%1 CheXie2024
%A Chen, Yi-Hsin
%A Xie, Hong-Sheng
%A Chen, Cheng-Wei
%A Gao, Zong-Lin
%A Benjak, Martin
%A Peng, Wen-Hsiao
%A Ostermann, J{ö}rn
%D 2024
%J IEEE Transactions on Circuits and Systems for Video Technology
%T Maskcrt: Masked conditional residual transformer for learned video compression - Adhisantoso, Y. G., and Cheung, P. (2024)Continuation of the MPEG-G Part 6 Specification Verification and Enhancement Study - m68660, ISO/IEC JTC 1/SC 29/WG 8.
@article{AdhChe2024,
author = {Adhisantoso, Yeremia Gunawan and Cheung, Patrick},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {leibnizailab},
month = {07},
title = {Continuation of the MPEG-G Part 6 Specification Verification and Enhancement Study - m68660},
year = 2024
}%0 Journal Article
%1 AdhChe2024
%A Adhisantoso, Yeremia Gunawan
%A Cheung, Patrick
%D 2024
%J ISO/IEC JTC 1/SC 29/WG 8
%T Continuation of the MPEG-G Part 6 Specification Verification and Enhancement Study - m68660 - Mahlau, Y., Schubert, F., Bethmann, K., Caspary, R., Lesina, A. C., Munderloh, M., Ostermann, J., and Rosenhahn, B. (2024)A flexible framework for large-scale FDTD simulations: open-source inverse design for 3D nanostructures, Preprint.
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author = {Mahlau, Yannik and Schubert, Frederik and Bethmann, Konrad and Caspary, Reinhard and Lesina, Antonio Calà and Munderloh, Marco and Ostermann, J{ö}rn and Rosenhahn, Bodo},
journal = {Preprint},
keywords = {leibnizailab},
month = 12,
title = {A flexible framework for large-scale FDTD simulations: open-source inverse design for 3D nanostructures},
year = 2024
}%0 Journal Article
%1 MahSch2024
%A Mahlau, Yannik
%A Schubert, Frederik
%A Bethmann, Konrad
%A Caspary, Reinhard
%A Lesina, Antonio Calà
%A Munderloh, Marco
%A Ostermann, J{ö}rn
%A Rosenhahn, Bodo
%D 2024
%J Preprint
%T A flexible framework for large-scale FDTD simulations: open-source inverse design for 3D nanostructures
2023
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booktitle = {Second International Conference on Automated Machine Learning},
keywords = {AutoRL},
month = {05},
pages = {1-14},
title = {AutoRL Hyperparameter Landscapes},
year = 2023
}%0 Conference Paper
%1 MohBen2023
%A Mohan, Aditya
%A Benjamins, Carolin
%A Wienecke, Konrad
%A Dockhorn, Alexander
%A Lindauer, Marius
%B Second International Conference on Automated Machine Learning
%D 2023
%P 1-14
%R 10.48550/arXiv.2304.02396
%T AutoRL Hyperparameter Landscapes - Glandorf*, P., Kaiser*, T., Rosenhahn, B., and (*contributed equally). (2023)HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization. In International Conference on Computer Vision Workshops (ICCVW).
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author = {Glandorf*, Patrick and Kaiser*, Timo and Rosenhahn, Bodo and (*contributed equally)},
booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
keywords = {Neural},
month = 10,
title = {HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization},
year = 2023
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%A Glandorf*, Patrick
%A Kaiser*, Timo
%A Rosenhahn, Bodo
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%B International Conference on Computer Vision Workshops (ICCVW)
%D 2023
%T HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization - Hinrichs, R., Sitcheu, A. J. Y., and Ostermann, J. (2023)Continuous Sign-Language Recognition using Transformers and Augmented Pose Estimation. In Proceedings of the International Conference on Pattern Recognition Applications and Methods.
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booktitle = {Proceedings of the International Conference on Pattern Recognition Applications and Methods},
keywords = {recognition},
title = {Continuous Sign-Language Recognition using Transformers and Augmented Pose Estimation},
year = 2023
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%A Hinrichs, Reemt
%A Sitcheu, Angelo Jovin Yamachui
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%D 2023
%T Continuous Sign-Language Recognition using Transformers and Augmented Pose Estimation - Cong, Y., Yi, J., Rosenhahn, B., and Yang, M. (2023)SSGVS: Semantic Scene Graph-to-Video Synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
keywords = {Graph-to-Video},
title = {SSGVS: Semantic Scene Graph-to-Video Synthesis},
year = 2023
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%1 ConYi2023
%A Cong, Yuren
%A Yi, Jinhui
%A Rosenhahn, Bodo
%A Yang, Michael
%B Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
%D 2023
%T SSGVS: Semantic Scene Graph-to-Video Synthesis - Hinrichs, R., Bilsky, J., and Ostermann, J. (2023)Vector-Quantized Feedback Recurrent Autoencoders for the Compression of the Stimulation Patterns of Cochlear Implants at Zero Delay. In Proceedings of the 24th International Conference on Digital Signal Processing.
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keywords = {Cochlear},
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title = {Vector-Quantized Feedback Recurrent Autoencoders for the Compression of the Stimulation Patterns of Cochlear Implants at Zero Delay},
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%A Hinrichs, Reemt
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%B Proceedings of the 24th International Conference on Digital Signal Processing
%D 2023
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title = {PEKORA: High-Performance 3D Genome Reconstruction Using K-th Order Spearman's Rank Correlation Approximation [Talk]},
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%D 2023
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pages = {73-75},
title = {Explainable AI for Games},
volume = 12,
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%1 ZhuAwi2023
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%D 2023
%J Human-Game AI Interaction (Dagstuhl Seminar 22251)
%N 6
%P 73-75
%R 10.4230/DagRep.12.6.28
%T Explainable AI for Games
%V 12 - Rosenhahn, B., and Osborne, T. (2023)Monte Carlo Graph Search for Quantum Circuit Optimization (Accepted), Physical Review A.
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author = {Rosenhahn, Bodo and Osborne, Tobias},
journal = {Physical Review A},
keywords = {monte},
month = 12,
title = {Monte Carlo Graph Search for Quantum Circuit Optimization (Accepted)},
year = 2023
}%0 Journal Article
%1 RosOsb2023a
%A Rosenhahn, Bodo
%A Osborne, Tobias
%D 2023
%J Physical Review A
%T Monte Carlo Graph Search for Quantum Circuit Optimization (Accepted) - Gebauer, C., Rumberg, L., Ehlert, H., L{ü}dtke, U., and Ostermann, J. (2023)Exploiting Diversity of Automatic Transcripts from Distinct Speech Recognition Techniques for Children’s Speech. In Accepted to Interspeech 2023.
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booktitle = {Accepted to Interspeech 2023},
keywords = {Speech},
month = {08},
title = {Exploiting Diversity of Automatic Transcripts from Distinct Speech Recognition Techniques for Children’s Speech},
year = 2023
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%1 GebRum2023b
%A Gebauer, Christopher
%A Rumberg, Lars
%A Ehlert, Hanna
%A L{ü}dtke, Ulrike
%A Ostermann, J{ö}rn
%B Accepted to Interspeech 2023
%D 2023
%T Exploiting Diversity of Automatic Transcripts from Distinct Speech Recognition Techniques for Children’s Speech - Auer, S., Barone, D. A. C., Bartz, C., Cortes, E. G., Jaradeh, M. Y., Karras, O., Koubarakis, M., Mouromtsev, D., Pliukhin, D., Radyush, D., Shilin, I., Stocker, M., and Tsalapati, E. (2023, March)SciQA benchmark: Dataset and {RDF} dump (Version 5), Zenodo.
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author = {Auer, S{{ö}}ren and Barone, Dante A. C. and Bartz, Cassiano and Cortes, Eduardo G. and Jaradeh, Mohamad Yaser and Karras, Oliver and Koubarakis, Manolis and Mouromtsev, Dmitry and Pliukhin, Dmitrii and Radyush, Daniil and Shilin, Ivan and Stocker, Markus and Tsalapati, Eleni},
howpublished = {\url{https://doi.org/10.5281/zenodo.7727922}},
keywords = {leibnizailab},
month = {03},
note = {Accessed on YYYY-MM-DD.},
publisher = {Zenodo},
title = {SciQA benchmark: Dataset and {RDF} dump (Version 5)},
year = 2023
}%0 Generic
%1 DBLP:data/10/AuerBBCJKKMPRSST23a
%A Auer, S{{ö}}ren
%A Barone, Dante A. C.
%A Bartz, Cassiano
%A Cortes, Eduardo G.
%A Jaradeh, Mohamad Yaser
%A Karras, Oliver
%A Koubarakis, Manolis
%A Mouromtsev, Dmitry
%A Pliukhin, Dmitrii
%A Radyush, Daniil
%A Shilin, Ivan
%A Stocker, Markus
%A Tsalapati, Eleni
%D 2023
%I Zenodo
%R 10.5281/ZENODO.7727922
%T SciQA benchmark: Dataset and {RDF} dump (Version 5)
%U https://doi.org/10.5281/zenodo.7727922 - Ehlert, H., Beaulac, E., Wallbaum, M., Gebauer, C., Rumberg, L., Ostermann, J., and L{ü}dtke, U. (2023)Collecting and Annotating Natural Child Speech Data – Challenges and Interdisciplinary Perspectives. In Elektronische Sprachsignalverarbeitung (ESSV), pp. 72–78.
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author = {Ehlert, Hanna and Beaulac, Edith and Wallbaum, Maren and Gebauer, Christopher and Rumberg, Lars and Ostermann, J{ö}rn and L{ü}dtke, Ulrike},
booktitle = {Elektronische Sprachsignalverarbeitung (ESSV)},
keywords = {Natural},
month = {03},
pages = {72--78},
title = {Collecting and Annotating Natural Child Speech Data – Challenges and Interdisciplinary Perspectives},
year = 2023
}%0 Conference Paper
%1 EhlBea2023a
%A Ehlert, Hanna
%A Beaulac, Edith
%A Wallbaum, Maren
%A Gebauer, Christopher
%A Rumberg, Lars
%A Ostermann, J{ö}rn
%A L{ü}dtke, Ulrike
%B Elektronische Sprachsignalverarbeitung (ESSV)
%D 2023
%P 72--78
%T Collecting and Annotating Natural Child Speech Data – Challenges and Interdisciplinary Perspectives
%@ 978-3-95908-303-4 - Schier, M., Reinders, C., and Rosenhahn, B. (2023)Deep Reinforcement Learning for Autonomous Driving Using High-Level Heterogeneous Graph Representations. In International Conference on Robotics and Automation (ICRA), p. to appear.
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author = {Schier, Maximilian and Reinders, Christoph and Rosenhahn, Bodo},
booktitle = {International Conference on Robotics and Automation (ICRA)},
keywords = {Deep},
pages = {to appear},
title = {Deep Reinforcement Learning for Autonomous Driving Using High-Level Heterogeneous Graph Representations},
year = 2023
}%0 Conference Paper
%1 SchRei2023a
%A Schier, Maximilian
%A Reinders, Christoph
%A Rosenhahn, Bodo
%B International Conference on Robotics and Automation (ICRA)
%D 2023
%P to appear
%T Deep Reinforcement Learning for Autonomous Driving Using High-Level Heterogeneous Graph Representations - Hachmann, H., and Rosenhahn, B. (2023)Color-aware Deep Temporal Backdrop Duplex Matting System. In MMSys ’23: Proceedings of the 14th ACM Multimedia Systems Conference.
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author = {Hachmann, Hendrik and Rosenhahn, Bodo},
booktitle = {MMSys '23: Proceedings of the 14th ACM Multimedia Systems Conference},
keywords = {Backdrop},
month = {06},
title = {Color-aware Deep Temporal Backdrop Duplex Matting System},
year = 2023
}%0 Conference Paper
%1 HacRos2023
%A Hachmann, Hendrik
%A Rosenhahn, Bodo
%B MMSys '23: Proceedings of the 14th ACM Multimedia Systems Conference
%D 2023
%T Color-aware Deep Temporal Backdrop Duplex Matting System - Safikhani, P., Avetisyan, H., F{ö}ste-Eggers, D., and Broneske, D. (2023)Automated occupation coding with hierarchical features: A data-centric approach to classification with pre-trained language models., Discover Artificial Intelligence.
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author = {Safikhani, Parisa and Avetisyan, Hayastan and F{ö}ste-Eggers, Dennis and Broneske, David},
journal = {Discover Artificial Intelligence},
keywords = {automated},
month = {01},
number = 3,
title = {Automated occupation coding with hierarchical features: A data-centric approach to classification with pre-trained language models.},
year = 2023
}%0 Journal Article
%1 SafAve2023
%A Safikhani, Parisa
%A Avetisyan, Hayastan
%A F{ö}ste-Eggers, Dennis
%A Broneske, David
%D 2023
%J Discover Artificial Intelligence
%N 3
%R https://doi.org/10.1007/s44163-023-00050-y
%T Automated occupation coding with hierarchical features: A data-centric approach to classification with pre-trained language models. - Rumberg, L., Gebauer, C., Ehlert, H., Wallbaum, M., L{ü}dtke, U., and Ostermann, J. (2023)Uncertainty Estimation for Connectionist Temporal Classification Based Automatic Speech Recognition. In Accepted to Interspeech 2023.
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author = {Rumberg, Lars and Gebauer, Christopher and Ehlert, Hanna and Wallbaum, Maren and L{ü}dtke, Ulrike and Ostermann, J{ö}rn},
booktitle = {Accepted to Interspeech 2023},
keywords = {Recognition},
month = {08},
title = {Uncertainty Estimation for Connectionist Temporal Classification Based Automatic Speech Recognition},
year = 2023
}%0 Conference Paper
%1 RumGeb2023a
%A Rumberg, Lars
%A Gebauer, Christopher
%A Ehlert, Hanna
%A Wallbaum, Maren
%A L{ü}dtke, Ulrike
%A Ostermann, J{ö}rn
%B Accepted to Interspeech 2023
%D 2023
%T Uncertainty Estimation for Connectionist Temporal Classification Based Automatic Speech Recognition - Rudolph, M., Wehrbein, T., Rosenhahn, B., and Wandt, B. (2023)Asymmetric Student-Teacher Networks for Industrial Anomaly Detection. In Winter Conference on Applications of Computer Vision (WACV).
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booktitle = {Winter Conference on Applications of Computer Vision (WACV)},
keywords = {Asymmetric},
month = {01},
title = {Asymmetric Student-Teacher Networks for Industrial Anomaly Detection},
year = 2023
}%0 Conference Paper
%1 RudWeh2023
%A Rudolph, Marco
%A Wehrbein, Tom
%A Rosenhahn, Bodo
%A Wandt, Bastian
%B Winter Conference on Applications of Computer Vision (WACV)
%D 2023
%T Asymmetric Student-Teacher Networks for Industrial Anomaly Detection
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@inproceedings{DBLP:conf/jcdl/KabongoDA23,
author = {Kabongo, Salomon and D'Souza, Jennifer and Auer, S{{ö}}ren},
booktitle = {{ACM/IEEE} Joint Conference on Digital Libraries, {JCDL} 2023, Santa Fe, NM, USA, June 26-30, 2023},
keywords = {leibnizailab},
pages = {237--241},
publisher = {{IEEE}},
title = {Zero-Shot Entailment of Leaderboards for Empirical {AI} Research},
year = 2023
}%0 Conference Paper
%1 DBLP:conf/jcdl/KabongoDA23
%A Kabongo, Salomon
%A D'Souza, Jennifer
%A Auer, S{{ö}}ren
%B {ACM/IEEE} Joint Conference on Digital Libraries, {JCDL} 2023, Santa Fe, NM, USA, June 26-30, 2023
%D 2023
%I {IEEE}
%P 237--241
%R 10.1109/JCDL57899.2023.00042
%T Zero-Shot Entailment of Leaderboards for Empirical {AI} Research
%U https://doi.org/10.1109/JCDL57899.2023.00042 - Nandy, A., Kapadnis, M. N., Goyal, P., and Ganguly, N. (2023)CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text. In The 2023 Conference on Empirical Methods in Natural Language Processing.
@inproceedings{nandy2023textbfemphclmsm,
author = {Nandy, Abhilash and Kapadnis, Manav Nitin and Goyal, Pawan and Ganguly, Niloy},
booktitle = {The 2023 Conference on Empirical Methods in Natural Language Processing},
keywords = {pretraining},
title = {CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text},
year = 2023
}%0 Conference Paper
%1 nandy2023textbfemphclmsm
%A Nandy, Abhilash
%A Kapadnis, Manav Nitin
%A Goyal, Pawan
%A Ganguly, Niloy
%B The 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%T CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text
%U https://openreview.net/forum?id=SP8zIwanHD - Roy, S., Wallat, J., Sundaram, S. S., Nejdl, W., and Ganguly, N. (2023)GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning. In Frontiers in Artificial Intelligence and Applications, pp. 2002–2009.Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and locally select the span around the mask center with the highest Normalized Pointwise Mutual Information (NPMI) to mask. We observe that in the absence of human-understandable semantics in the genomics domain (in contrast, semantic units like words and phrases are inherently available in NLP), GENEMASK-based models substantially outperform the SOTA models (DNABert and LOGO) over four benchmark gene sequence classification datasets in five few-shot settings (10 to 1000-shot). More significantly, the GENEMASK-based DNABert model is trained for less than one-tenth of the number of epochs of the original SOTA model. We also observe a strong correlation between top-ranked PMI tokens and conserved DNA sequence motifs, which may indicate the incorporation of latent genomic information. The codes (including trained models) and datasets are made publicly available at unmapped: uri https://github.com/roysoumya/GeneMask.
@inproceedings{noauthororeditor,
abstract = {Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and locally select the span around the mask center with the highest Normalized Pointwise Mutual Information (NPMI) to mask. We observe that in the absence of human-understandable semantics in the genomics domain (in contrast, semantic units like words and phrases are inherently available in NLP), GENEMASK-based models substantially outperform the SOTA models (DNABert and LOGO) over four benchmark gene sequence classification datasets in five few-shot settings (10 to 1000-shot). More significantly, the GENEMASK-based DNABert model is trained for less than one-tenth of the number of epochs of the original SOTA model. We also observe a strong correlation between top-ranked PMI tokens and conserved DNA sequence motifs, which may indicate the incorporation of latent genomic information. The codes (including trained models) and datasets are made publicly available at unmapped: uri https://github.com/roysoumya/GeneMask.},
author = {Roy, Soumyadeep and Wallat, Jonas and Sundaram, Sowmya S and Nejdl, Wolfgang and Ganguly, Niloy},
keywords = {l3s},
pages = {2002-2009},
series = {Frontiers in Artificial Intelligence and Applications},
title = {GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning},
volume = 372,
year = 2023
}%0 Conference Paper
%1 noauthororeditor
%A Roy, Soumyadeep
%A Wallat, Jonas
%A Sundaram, Sowmya S
%A Nejdl, Wolfgang
%A Ganguly, Niloy
%B Frontiers in Artificial Intelligence and Applications
%D 2023
%P 2002-2009
%R 10.3233/FAIA230492
%T GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning
%V 372
%X Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and locally select the span around the mask center with the highest Normalized Pointwise Mutual Information (NPMI) to mask. We observe that in the absence of human-understandable semantics in the genomics domain (in contrast, semantic units like words and phrases are inherently available in NLP), GENEMASK-based models substantially outperform the SOTA models (DNABert and LOGO) over four benchmark gene sequence classification datasets in five few-shot settings (10 to 1000-shot). More significantly, the GENEMASK-based DNABert model is trained for less than one-tenth of the number of epochs of the original SOTA model. We also observe a strong correlation between top-ranked PMI tokens and conserved DNA sequence motifs, which may indicate the incorporation of latent genomic information. The codes (including trained models) and datasets are made publicly available at unmapped: uri https://github.com/roysoumya/GeneMask.
%@ 978-1-64368-437-6 - Xiao, J., Basso, L., Nejdl, W., Ganguly, N., and Sikdar, S. (2023)IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers, arXiv preprint arXiv:2305.06741.
@preprint{xiao2023ivpvae,
author = {Xiao, Jingge and Basso, Leonie and Nejdl, Wolfgang and Ganguly, Niloy and Sikdar, Sandipan},
journal = {arXiv preprint arXiv:2305.06741},
keywords = {leibnizailab},
title = {IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers},
year = 2023
}%0 Generic
%1 xiao2023ivpvae
%A Xiao, Jingge
%A Basso, Leonie
%A Nejdl, Wolfgang
%A Ganguly, Niloy
%A Sikdar, Sandipan
%D 2023
%J arXiv preprint arXiv:2305.06741
%T IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers - Gebauer, C., Rumberg, L., and Ostermann, J. (2023)Pronunciation Modeling for Children’s Speech. In Elektronische Sprachsignalverarbeitung (ESSV), pp. 79–86.
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author = {Gebauer, Christopher and Rumberg, Lars and Ostermann, J{ö}rn},
booktitle = {Elektronische Sprachsignalverarbeitung (ESSV)},
keywords = {Modeling},
month = {03},
pages = {79--86},
title = {Pronunciation Modeling for Children’s Speech},
year = 2023
}%0 Conference Paper
%1 GebRum2023
%A Gebauer, Christopher
%A Rumberg, Lars
%A Ostermann, J{ö}rn
%B Elektronische Sprachsignalverarbeitung (ESSV)
%D 2023
%P 79--86
%T Pronunciation Modeling for Children’s Speech
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@conference{reneffective,
author = {Ren, Zhao and Nguyen, Thanh Tam and Chang, Yi and Schuller, Björn W.},
booktitle = {ICASSP},
keywords = {leibnizailab},
title = {Fast yet effective speech emotion recognition with self-distillation},
year = 2023
}%0 Generic
%1 reneffective
%A Ren, Zhao
%A Nguyen, Thanh Tam
%A Chang, Yi
%A Schuller, Björn W.
%B ICASSP
%D 2023
%T Fast yet effective speech emotion recognition with self-distillation - Xu, L., Dockhorn, A., and Perez-Liebana, D. (2023)Elastic Monte Carlo Tree Search, IEEE Transactions on Games.
@article{XuDoc2023,
author = {Xu, Linjie and Dockhorn, Alexander and Perez-Liebana, Diego},
journal = {IEEE Transactions on Games},
keywords = {Monte},
note = {(to be printed)},
title = {Elastic Monte Carlo Tree Search},
year = 2023
}%0 Journal Article
%1 XuDoc2023
%A Xu, Linjie
%A Dockhorn, Alexander
%A Perez-Liebana, Diego
%D 2023
%J IEEE Transactions on Games
%R 10.1109/TG.2023.3282351
%T Elastic Monte Carlo Tree Search - M{ü}ntefering, F., Ostermann, J., and Voges, J. (2023)BACON: Bacterial Clone Recognition from Metagenomic Sequencing Data, AICPM 2023.
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journal = {AICPM 2023},
keywords = {leibnizailab},
month = {09},
title = {BACON: Bacterial Clone Recognition from Metagenomic Sequencing Data},
year = 2023
}%0 Journal Article
%1 MueOst2023a
%A M{ü}ntefering, Fabian
%A Ostermann, J{ö}rn
%A Voges, Jan
%D 2023
%J AICPM 2023
%T BACON: Bacterial Clone Recognition from Metagenomic Sequencing Data - Xie, H.-S., Chen, Y.-H., Peng, W.-H., Benjak, M., and Ostermann, J. (2023)Rate Adaptation for Learned Two-layer B-frame Coding without Signaling Motion Information. In IEEE International Conference on Visual Communications and Image Processing (VCIP).
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author = {Xie, Hong-Sheng and Chen, Yi-Hsin and Peng, Wen-Hsiao and Benjak, Martin and Ostermann, J{ö}rn},
booktitle = {IEEE International Conference on Visual Communications and Image Processing (VCIP)},
keywords = {rate},
title = {Rate Adaptation for Learned Two-layer B-frame Coding without Signaling Motion Information},
year = 2023
}%0 Conference Paper
%1 XieChe2023a
%A Xie, Hong-Sheng
%A Chen, Yi-Hsin
%A Peng, Wen-Hsiao
%A Benjak, Martin
%A Ostermann, J{ö}rn
%B IEEE International Conference on Visual Communications and Image Processing (VCIP)
%D 2023
%T Rate Adaptation for Learned Two-layer B-frame Coding without Signaling Motion Information - Hachmann, H., and Rosenhahn, B. (2023)Human Spine Motion Capture using Perforated Kinesiology Tape. In Computer Vision and Pattern Recognition Workshops (CVPRW).
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keywords = {Spine},
month = {06},
title = {Human Spine Motion Capture using Perforated Kinesiology Tape},
year = 2023
}%0 Conference Paper
%1 HacRos2023a
%A Hachmann, Hendrik
%A Rosenhahn, Bodo
%B Computer Vision and Pattern Recognition Workshops (CVPRW)
%D 2023
%T Human Spine Motion Capture using Perforated Kinesiology Tape - W{ö}rz, N., Woiwode, D., Sondheim, J., Behrens, D., Rudy, D., and Gl{ü}cksklee, T. (2023)Pflanzenforschung an Bord der ISS, BIOspektrum 29 29, 557.
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author = {W{ö}rz, Nils and Woiwode, Dominik and Sondheim, Justin and Behrens, D{ö}rthe and Rudy, Dorian and Gl{ü}cksklee, Team},
journal = {BIOspektrum 29},
keywords = {ISS},
month = {09},
number = 5,
pages = 557,
title = {Pflanzenforschung an Bord der ISS},
volume = 29,
year = 2023
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%1 WoeWoi2023
%A W{ö}rz, Nils
%A Woiwode, Dominik
%A Sondheim, Justin
%A Behrens, D{ö}rthe
%A Rudy, Dorian
%A Gl{ü}cksklee, Team
%D 2023
%J BIOspektrum 29
%N 5
%P 557
%R 10.1007/s12268-023-1972-1
%T Pflanzenforschung an Bord der ISS
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author = {Kuhnke, Felix and Ostermann, J{ö}rn},
journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
keywords = {Adaptation},
title = {Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency},
year = 2023
}%0 Journal Article
%1 KuhOst2023
%A Kuhnke, Felix
%A Ostermann, J{ö}rn
%D 2023
%J IEEE Transactions on Biometrics, Behavior, and Identity Science
%R 10.1109/TBIOM.2023.3237039
%T Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency - Benjamins, C., Eimer, T., Schubert, F., Mohan, A., D{ö}hler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2023)Contextualize Me - The Case for Context in Reinforcement Learning, Transactions on Machine Learning Research.
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author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Mohan, Aditya and D{ö}hler, Sebastian and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
journal = {Transactions on Machine Learning Research},
keywords = {reinforcement},
month = {06},
title = {Contextualize Me - The Case for Context in Reinforcement Learning},
year = 2023
}%0 Journal Article
%1 BenEim2023
%A Benjamins, Carolin
%A Eimer, Theresa
%A Schubert, Frederik
%A Mohan, Aditya
%A D{ö}hler, Sebastian
%A Biedenkapp, André
%A Rosenhahn, Bodo
%A Hutter, Frank
%A Lindauer, Marius
%D 2023
%J Transactions on Machine Learning Research
%T Contextualize Me - The Case for Context in Reinforcement Learning
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author = {Safikhani, Parisa and Broneske, David},
journal = {International Conference on Machine Learning Techniques and NLP},
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title = {Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch.},
volume = 13,
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%1 SafBro2023
%A Safikhani, Parisa
%A Broneske, David
%D 2023
%J International Conference on Machine Learning Techniques and NLP
%N 16
%T Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch.
%V 13
%@ 978-1-923107-04-5 - Cong, Y., Yang, M., and Rosenhahn, B. (2023)RelTR: Relation Transformer for Scene Graph Generation, IEEE transactions on pattern analysis and machine intelligence (TPAMI).
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journal = {IEEE transactions on pattern analysis and machine intelligence (TPAMI)},
keywords = {Relation},
title = {RelTR: Relation Transformer for Scene Graph Generation},
year = 2023
}%0 Journal Article
%1 ConYan2023
%A Cong, Yuren
%A Yang, Michael
%A Rosenhahn, Bodo
%D 2023
%J IEEE transactions on pattern analysis and machine intelligence (TPAMI)
%T RelTR: Relation Transformer for Scene Graph Generation - Lee, C.-S., Wang, M.-H., Chen, C.-Y., Yang, F.-J., and Dockhorn, A. (2023)Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience. In 2023 IEEE Congress on Evolutionary Computation, pp. 1–8.
@inproceedings{LeeWan2023,
author = {Lee, Chang-Shing and Wang, Mei-Hui and Chen, Chih-Yu and Yang, Fu-Jie and Dockhorn, Alexander},
booktitle = {2023 IEEE Congress on Evolutionary Computation},
keywords = {Student},
note = {(to be published)},
pages = {1-8},
title = {Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience},
year = 2023
}%0 Conference Paper
%1 LeeWan2023
%A Lee, Chang-Shing
%A Wang, Mei-Hui
%A Chen, Chih-Yu
%A Yang, Fu-Jie
%A Dockhorn, Alexander
%B 2023 IEEE Congress on Evolutionary Computation
%D 2023
%P 1-8
%T Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience - Awiszus, M., Dockhorn, A., Hoover, A. K., Liapis, A., Lucas, S. M., Eladhari, M. P., Schrum, J., and Volz, V. (2023)Language Models for Procedural Content Generation, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 34–37.
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author = {Awiszus, Maren and Dockhorn, Alexander and Hoover, Amy K. and Liapis, Antonios and Lucas, Simon M. and Eladhari, Mirjam Palosaari and Schrum, Jacob and Volz, Vanessa},
journal = {Human-Game AI Interaction (Dagstuhl Seminar 22251)},
keywords = {Language},
month = {01},
number = 6,
pages = {34-37},
title = {Language Models for Procedural Content Generation},
volume = 12,
year = 2023
}%0 Journal Article
%1 AwiDoc2023
%A Awiszus, Maren
%A Dockhorn, Alexander
%A Hoover, Amy K.
%A Liapis, Antonios
%A Lucas, Simon M.
%A Eladhari, Mirjam Palosaari
%A Schrum, Jacob
%A Volz, Vanessa
%D 2023
%J Human-Game AI Interaction (Dagstuhl Seminar 22251)
%N 6
%P 34-37
%R 10.4230/DagRep.12.6.28
%T Language Models for Procedural Content Generation
%V 12 - Dockhorn, A., Eberhardinger, M., Loiacono, D., Liebana, D. P., and Veltkamp, R. (2023)Pokegen, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 39–42.
@article{DocEbe2023a,
author = {Dockhorn, Alexander and Eberhardinger, Manuel and Loiacono, Daniele and Liebana, Diego Perez and Veltkamp, Remco},
journal = {Human-Game AI Interaction (Dagstuhl Seminar 22251)},
keywords = {Pokegen},
month = {01},
number = 6,
pages = {39-42},
title = {Pokegen},
volume = 12,
year = 2023
}%0 Journal Article
%1 DocEbe2023a
%A Dockhorn, Alexander
%A Eberhardinger, Manuel
%A Loiacono, Daniele
%A Liebana, Diego Perez
%A Veltkamp, Remco
%D 2023
%J Human-Game AI Interaction (Dagstuhl Seminar 22251)
%N 6
%P 39-42
%R 10.4230/DagRep.12.6.28
%T Pokegen
%V 12 - Kaiser, T., Reinders, C., and Rosenhahn, B. (2023)Compensation Learning in Semantic Segmentation. In Computer Vision and Pattern Recognition Workshops (CVPRW).
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keywords = {Segmention},
month = {06},
title = {Compensation Learning in Semantic Segmentation},
year = 2023
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%1 KaiRei2023a
%A Kaiser, Timo
%A Reinders, Christoph
%A Rosenhahn, Bodo
%B Computer Vision and Pattern Recognition Workshops (CVPRW)
%D 2023
%T Compensation Learning in Semantic Segmentation - Adhisantoso, Y. G., and Voges, J. (2023)Cross-check of M62859 Results on Updated CE Results for Annotation Data Indexing Using B-Tree, ISO/IEC JTC 1/SC 29/WG 8.
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month = {04},
title = {Cross-check of M62859 Results on Updated CE Results for Annotation Data Indexing Using B-Tree},
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}%0 Journal Article
%1 AdhVog2023a
%A Adhisantoso, Yeremia Gunawan
%A Voges, Jan
%D 2023
%J ISO/IEC JTC 1/SC 29/WG 8
%T Cross-check of M62859 Results on Updated CE Results for Annotation Data Indexing Using B-Tree - Jaradeh, M. Y., Singh, K., Stocker, M., Both, A., and Auer, S. (2023)Information extraction pipelines for knowledge graphs, Knowl. Inf. Syst. 65, 1989–2016.
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journal = {Knowl. Inf. Syst.},
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number = 5,
pages = {1989--2016},
title = {Information extraction pipelines for knowledge graphs},
volume = 65,
year = 2023
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%1 DBLP:journals/kais/JaradehSSBA23
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%A Singh, Kuldeep
%A Stocker, Markus
%A Both, Andreas
%A Auer, S{{ö}}ren
%D 2023
%J Knowl. Inf. Syst.
%N 5
%P 1989--2016
%R 10.1007/S10115-022-01826-X
%T Information extraction pipelines for knowledge graphs
%U https://doi.org/10.1007/s10115-022-01826-x
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author = {Chang, Yi and Ren, Zhao and Nguyen, Thanh Tam and Qian, Kun and Schuller, Björn W.},
booktitle = {ICASSP},
keywords = {leibnizailab},
title = {Knowledge transfer for on-device speech emotion recognition with neural structured learning},
year = 2023
}%0 Generic
%1 noauthororeditor2023knowledge
%A Chang, Yi
%A Ren, Zhao
%A Nguyen, Thanh Tam
%A Qian, Kun
%A Schuller, Björn W.
%B ICASSP
%D 2023
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@inproceedings{DBLP:conf/dexa/DSouzaHA23,
author = {D'Souza, Jennifer and Hrou, Moussab and Auer, S{{ö}}ren},
booktitle = {Database and Expert Systems Applications - 34th International Conference, {DEXA} 2023, Penang, Malaysia, August 28-30, 2023, Proceedings, Part {I}},
editor = {Strauss, Christine and Amagasa, Toshiyuki and Kotsis, Gabriele and Tjoa, A Min and Khalil, Ismail},
keywords = {leibnizailab},
pages = {508--515},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {Evaluating Prompt-Based Question Answering for Object Prediction in the Open Research Knowledge Graph},
volume = 14146,
year = 2023
}%0 Conference Paper
%1 DBLP:conf/dexa/DSouzaHA23
%A D'Souza, Jennifer
%A Hrou, Moussab
%A Auer, S{{ö}}ren
%B Database and Expert Systems Applications - 34th International Conference, {DEXA} 2023, Penang, Malaysia, August 28-30, 2023, Proceedings, Part {I}
%D 2023
%E Strauss, Christine
%E Amagasa, Toshiyuki
%E Kotsis, Gabriele
%E Tjoa, A Min
%E Khalil, Ismail
%I Springer
%P 508--515
%R 10.1007/978-3-031-39847-6\_40
%T Evaluating Prompt-Based Question Answering for Object Prediction in the Open Research Knowledge Graph
%U https://doi.org/10.1007/978-3-031-39847-6\_40
%V 14146 - Ekaputra, F. J., Llugiqi, M., Sabou, M., Ekelhart, A., Paulheim, H., Breit, A., Revenko, A., Waltersdorfer, L., Farfar, K. E., and Auer, S. (2023)Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG. In The Semantic Web - 20th International Conference, {ESWC} 2023, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings (Pesquita, C., Jim{{é}}nez{-}Ruiz, E., McCusker, J. P., Faria, D., Dragoni, M., Dimou, A., Troncy, R., and Hertling, S., Eds.), pp. 372–389, Springer.
@inproceedings{DBLP:conf/esws/EkaputraLSEPBRWFA23,
author = {Ekaputra, Fajar J. and Llugiqi, Majlinda and Sabou, Marta and Ekelhart, Andreas and Paulheim, Heiko and Breit, Anna and Revenko, Artem and Waltersdorfer, Laura and Farfar, Kheir Eddine and Auer, S{{ö}}ren},
booktitle = {The Semantic Web - 20th International Conference, {ESWC} 2023, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings},
editor = {Pesquita, Catia and Jim{{é}}nez{-}Ruiz, Ernesto and McCusker, Jamie P. and Faria, Daniel and Dragoni, Mauro and Dimou, Anastasia and Troncy, Rapha{{ë}}l and Hertling, Sven},
keywords = {leibnizailab},
pages = {372--389},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG},
volume = 13870,
year = 2023
}%0 Conference Paper
%1 DBLP:conf/esws/EkaputraLSEPBRWFA23
%A Ekaputra, Fajar J.
%A Llugiqi, Majlinda
%A Sabou, Marta
%A Ekelhart, Andreas
%A Paulheim, Heiko
%A Breit, Anna
%A Revenko, Artem
%A Waltersdorfer, Laura
%A Farfar, Kheir Eddine
%A Auer, S{{ö}}ren
%B The Semantic Web - 20th International Conference, {ESWC} 2023, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings
%D 2023
%E Pesquita, Catia
%E Jim{{é}}nez{-}Ruiz, Ernesto
%E McCusker, Jamie P.
%E Faria, Daniel
%E Dragoni, Mauro
%E Dimou, Anastasia
%E Troncy, Rapha{{ë}}l
%E Hertling, Sven
%I Springer
%P 372--389
%R 10.1007/978-3-031-33455-9\_22
%T Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG
%U https://doi.org/10.1007/978-3-031-33455-9\_22
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author = {Olson, Christopher and Wagner, Lars and Dockhorn, Alexander},
booktitle = {2023 IEEE Congress on Evolutionary Computation (CEC)},
keywords = {You},
note = {(to be published)},
pages = {1-8},
title = {Evolutionary Optimization of Baba Is You Agents},
year = 2023
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%1 OlsWag2023
%A Olson, Christopher
%A Wagner, Lars
%A Dockhorn, Alexander
%B 2023 IEEE Congress on Evolutionary Computation (CEC)
%D 2023
%P 1-8
%T Evolutionary Optimization of Baba Is You Agents - Cook, M., Awiszus, M., Cakmak, D., Denisova, A., Dockhorn, A., Harteveld, C., Liapis, A., Eladhari, M. P., Liebana, D. P., Rombout, L., and Thompson, T. (2023)AI for Romantic Comedies, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 37–39.
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journal = {Human-Game AI Interaction (Dagstuhl Seminar 22251)},
keywords = {Romantic},
month = {01},
number = 6,
pages = {37-39},
title = {AI for Romantic Comedies},
volume = 12,
year = 2023
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%1 CooAwi2023
%A Cook, Michael
%A Awiszus, Maren
%A Cakmak, Duygu
%A Denisova, Alena
%A Dockhorn, Alexander
%A Harteveld, Casper
%A Liapis, Antonios
%A Eladhari, Mirjam Palosaari
%A Liebana, Diego Perez
%A Rombout, Lisa
%A Thompson, Tommy
%D 2023
%J Human-Game AI Interaction (Dagstuhl Seminar 22251)
%N 6
%P 37-39
%R 10.4230/DagRep.12.6.28
%T AI for Romantic Comedies
%V 12 - Liapis, A., Awiszus, M., Champandard, A. J., Cook, M., Denisova, A., Dockhorn, A., Thompson, T., and Zhu, J. (2023)Artificial Intelligence for Audiences, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 50–54.
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author = {Liapis, Antonios and Awiszus, Maren and Champandard, Alex J. and Cook, Michael and Denisova, Alena and Dockhorn, Alexander and Thompson, Tommy and Zhu, Jichen},
journal = {Human-Game AI Interaction (Dagstuhl Seminar 22251)},
keywords = {for},
month = {01},
number = 6,
pages = {50-54},
title = {Artificial Intelligence for Audiences},
volume = 12,
year = 2023
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%1 LiaAwi2023
%A Liapis, Antonios
%A Awiszus, Maren
%A Champandard, Alex J.
%A Cook, Michael
%A Denisova, Alena
%A Dockhorn, Alexander
%A Thompson, Tommy
%A Zhu, Jichen
%D 2023
%J Human-Game AI Interaction (Dagstuhl Seminar 22251)
%N 6
%P 50-54
%R 10.4230/DagRep.12.6.28
%T Artificial Intelligence for Audiences
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@misc{DBLP:data/10/AuerBBCJKKMPRSST23e,
author = {Auer, S{{ö}}ren and Barone, Dante A. C. and Bartz, Cassiano and Cortes, Eduardo G. and Jaradeh, Mohamad Yaser and Karras, Oliver and Koubarakis, Manolis and Mouromtsev, Dmitry and Pliukhin, Dmitrii and Radyush, Daniil and Shilin, Ivan and Stocker, Markus and Tsalapati, Eleni},
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author = {Giglou, Hamed Babaei and D'Souza, Jennifer and Auer, S{{ö}}ren},
booktitle = {The Semantic Web - {ISWC} 2023 - 22nd International Semantic Web Conference, Athens, Greece, November 6-10, 2023, Proceedings, Part {I}},
editor = {Payne, Terry R. and Presutti, Valentina and Qi, Guilin and Poveda{-}Villal{{ó}}n, Mar{\'{\i}}a and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong and Li, Juanzi},
keywords = {leibnizailab},
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publisher = {Springer},
series = {Lecture Notes in Computer Science},
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%A Giglou, Hamed Babaei
%A D'Souza, Jennifer
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%E Kaoudi, Zoi
%E Cheng, Gong
%E Li, Juanzi
%I Springer
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journal = {CoRR},
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title = {GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning},
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publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {Probing BERT for Ranking Abilities},
volume = 13981,
year = 2023
}%0 Conference Paper
%1 DBLP:conf/ecir/WallatBAA23
%A Wallat, Jonas
%A Beringer, Fabian
%A Anand, Abhijit
%A Anand, Avishek
%B Advances in Information Retrieval - 45th European Conference on Information Retrieval, {ECIR} 2023, Dublin, Ireland, April 2-6, 2023, Proceedings, Part {II}
%D 2023
%E Kamps, Jaap
%E Goeuriot, Lorraine
%E Crestani, Fabio
%E Maistro, Maria
%E Joho, Hideo
%E Davis, Brian
%E Gurrin, Cathal
%E Kruschwitz, Udo
%E Caputo, Annalina
%I Springer
%P 255--273
%R 10.1007/978-3-031-28238-6\_17
%T Probing BERT for Ranking Abilities
%U https://doi.org/10.1007/978-3-031-28238-6\_17
%V 13981 - Brockmann*, J. T., Rudolph*, M., Rosenhahn, B., Wandt, B., and equal contribution), (*. (2023)The voraus-AD Dataset for Anomaly Detection in Robot Applications, Transactions on Robotics.
@article{BroRud2023,
author = {Brockmann*, Jan Thie{{\"s}} and Rudolph*, Marco and Rosenhahn, Bodo and Wandt, Bastian and equal contribution), (*},
journal = {Transactions on Robotics},
keywords = {Robot},
month = 11,
title = {The voraus-AD Dataset for Anomaly Detection in Robot Applications},
year = 2023
}%0 Journal Article
%1 BroRud2023
%A Brockmann*, Jan Thie{{\"s}}
%A Rudolph*, Marco
%A Rosenhahn, Bodo
%A Wandt, Bastian
%A equal contribution), (*
%D 2023
%J Transactions on Robotics
%R 10.1109/TRO.2023.3332224
%T The voraus-AD Dataset for Anomaly Detection in Robot Applications - Schier, M., Reinders, C., and Rosenhahn, B. (2023)Learned Fourier Bases for Deep Set Feature Extractors in Automotive Reinforcement Learning. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), p. to appear.
@inproceedings{SchRei2023b,
author = {Schier, Maximilian and Reinders, Christoph and Rosenhahn, Bodo},
booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)},
keywords = {Automotive},
month = {09},
pages = {to appear},
title = {Learned Fourier Bases for Deep Set Feature Extractors in Automotive Reinforcement Learning},
year = 2023
}%0 Conference Paper
%1 SchRei2023b
%A Schier, Maximilian
%A Reinders, Christoph
%A Rosenhahn, Bodo
%B 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
%D 2023
%P to appear
%T Learned Fourier Bases for Deep Set Feature Extractors in Automotive Reinforcement Learning - Schubert, F., Benjamins, C., D{ö}hler, S., Rosenhahn, B., and Lindauer, M. (2023)POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning, Transactions on Machine Learning Research.
@article{SchBen2023,
author = {Schubert, Frederik and Benjamins, Carolin and D{ö}hler, Sebastian and Rosenhahn, Bodo and Lindauer, Marius},
journal = {Transactions on Machine Learning Research},
keywords = {Trajectory},
month = {04},
title = {POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning},
year = 2023
}%0 Journal Article
%1 SchBen2023
%A Schubert, Frederik
%A Benjamins, Carolin
%A D{ö}hler, Sebastian
%A Rosenhahn, Bodo
%A Lindauer, Marius
%D 2023
%J Transactions on Machine Learning Research
%T POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning - Lange, A., K{ä}ding, M., Xu, R., Marx, S., and Ostermann, J. (2023)Semi-supervised learning for acoustic emission monitoring of tendons in prestressed concrete bridges. In 14th International Workshop on Structural Health Monitoring (IWSHM).
@inproceedings{AleMax2023a,
author = {Lange, Alexander and K{ä}ding, Max and Xu, Ronghua and Marx, Steffen and Ostermann, J{ö}rn},
booktitle = {14th International Workshop on Structural Health Monitoring (IWSHM)},
keywords = {learning},
month = {09},
title = {Semi-supervised learning for acoustic emission monitoring of tendons in prestressed concrete bridges},
year = 2023
}%0 Conference Paper
%1 AleMax2023a
%A Lange, Alexander
%A K{ä}ding, Max
%A Xu, Ronghua
%A Marx, Steffen
%A Ostermann, J{ö}rn
%B 14th International Workshop on Structural Health Monitoring (IWSHM)
%D 2023
%T Semi-supervised learning for acoustic emission monitoring of tendons in prestressed concrete bridges - Ganguly, N., Fazlija, D., Badar, M., Fisichella, M., Sikdar, S., Schrader, J., Wallat, J., Rudra, K., Koubarakis, M., Patro, G. K., Amri, W. Z. E., and Nejdl, W. (2023)A Review of the Role of Causality in Developing Trustworthy AI Systems.State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.
@article{ganguly2023review,
abstract = {State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.},
author = {Ganguly, Niloy and Fazlija, Dren and Badar, Maryam and Fisichella, Marco and Sikdar, Sandipan and Schrader, Johanna and Wallat, Jonas and Rudra, Koustav and Koubarakis, Manolis and Patro, Gourab K. and Amri, Wadhah Zai El and Nejdl, Wolfgang},
keywords = {unpublished},
title = {A Review of the Role of Causality in Developing Trustworthy AI Systems},
year = 2023
}%0 Journal Article
%1 ganguly2023review
%A Ganguly, Niloy
%A Fazlija, Dren
%A Badar, Maryam
%A Fisichella, Marco
%A Sikdar, Sandipan
%A Schrader, Johanna
%A Wallat, Jonas
%A Rudra, Koustav
%A Koubarakis, Manolis
%A Patro, Gourab K.
%A Amri, Wadhah Zai El
%A Nejdl, Wolfgang
%D 2023
%T A Review of the Role of Causality in Developing Trustworthy AI Systems
%U https://arxiv.org/abs/2302.06975
%X State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI. - Hussein, H., Farfar, K. E., Oelen, A., Karras, O., and Auer, S. (2023)Increasing Reproducibility in Science by Interlinking Semantic Artifact Descriptions in a Knowledge Graph. In Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, {ICADL} 2023, Taipei, Taiwan, December 4-7, 2023, Proceedings, Part {II} (Goh, D. H.- }Lian, Chen, S.- }Jiun, and Tuarob, S., Eds.), pp. 220–229, Springer.
@inproceedings{DBLP:conf/icadl/HusseinFOKA23,
author = {Hussein, Hassan and Farfar, Kheir Eddine and Oelen, Allard and Karras, Oliver and Auer, S{{ö}}ren},
booktitle = {Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, {ICADL} 2023, Taipei, Taiwan, December 4-7, 2023, Proceedings, Part {II}},
editor = {Goh, Dion Hoe{-}Lian and Chen, Shu{-}Jiun and Tuarob, Suppawong},
keywords = {leibnizailab},
pages = {220--229},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {Increasing Reproducibility in Science by Interlinking Semantic Artifact Descriptions in a Knowledge Graph},
volume = 14458,
year = 2023
}%0 Conference Paper
%1 DBLP:conf/icadl/HusseinFOKA23
%A Hussein, Hassan
%A Farfar, Kheir Eddine
%A Oelen, Allard
%A Karras, Oliver
%A Auer, S{{ö}}ren
%B Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, {ICADL} 2023, Taipei, Taiwan, December 4-7, 2023, Proceedings, Part {II}
%D 2023
%E Goh, Dion Hoe{-}Lian
%E Chen, Shu{-}Jiun
%E Tuarob, Suppawong
%I Springer
%P 220--229
%R 10.1007/978-981-99-8088-8\_19
%T Increasing Reproducibility in Science by Interlinking Semantic Artifact Descriptions in a Knowledge Graph
%U https://doi.org/10.1007/978-981-99-8088-8\_19
%V 14458 - Fathalla, S., Lange, C., and Auer, S. (2023)An Upper Ontology for Modern Science Branches and Related Entities. In The Semantic Web - 20th International Conference, {ESWC} 2023, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings (Pesquita, C., Jim{{é}}nez{-}Ruiz, E., McCusker, J. P., Faria, D., Dragoni, M., Dimou, A., Troncy, R., and Hertling, S., Eds.), pp. 436–453, Springer.
@inproceedings{DBLP:conf/esws/FathallaLA23,
author = {Fathalla, Said and Lange, Christoph and Auer, S{{ö}}ren},
booktitle = {The Semantic Web - 20th International Conference, {ESWC} 2023, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings},
editor = {Pesquita, Catia and Jim{{é}}nez{-}Ruiz, Ernesto and McCusker, Jamie P. and Faria, Daniel and Dragoni, Mauro and Dimou, Anastasia and Troncy, Rapha{{ë}}l and Hertling, Sven},
keywords = {leibnizailab},
pages = {436--453},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {An Upper Ontology for Modern Science Branches and Related Entities},
volume = 13870,
year = 2023
}%0 Conference Paper
%1 DBLP:conf/esws/FathallaLA23
%A Fathalla, Said
%A Lange, Christoph
%A Auer, S{{ö}}ren
%B The Semantic Web - 20th International Conference, {ESWC} 2023, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings
%D 2023
%E Pesquita, Catia
%E Jim{{é}}nez{-}Ruiz, Ernesto
%E McCusker, Jamie P.
%E Faria, Daniel
%E Dragoni, Mauro
%E Dimou, Anastasia
%E Troncy, Rapha{{ë}}l
%E Hertling, Sven
%I Springer
%P 436--453
%R 10.1007/978-3-031-33455-9\_26
%T An Upper Ontology for Modern Science Branches and Related Entities
%U https://doi.org/10.1007/978-3-031-33455-9\_26
%V 13870 - Poker, Y., von Hardenberg, S., Hofmann, W., Tang, M., Baumann, U., Schwerk, N., Wetzke, M., Lindenthal, V., Auber, B., Schlegelberger, B., Ott, H., von Bismarck, P., Viemann, D., Dressler, F., Klemann, C., and Bergmann, A. K. (2023)Systematic genetic analysis of pediatric patients with autoinflammatory diseases, Frontiers in Genetics, Frontiers Media {SA} 14.
@article{Poker_2023,
author = {Poker, Yvonne and von Hardenberg, Sandra and Hofmann, Winfried and Tang, Ming and Baumann, Ulrich and Schwerk, Nicolaus and Wetzke, Martin and Lindenthal, Viola and Auber, Bernd and Schlegelberger, Brigitte and Ott, Hagen and von Bismarck, Philipp and Viemann, Dorothee and Dressler, Frank and Klemann, Christian and Bergmann, Anke Katharina},
journal = {Frontiers in Genetics},
keywords = {l3s},
month = {01},
publisher = {Frontiers Media {SA}},
title = {Systematic genetic analysis of pediatric patients with autoinflammatory diseases},
volume = 14,
year = 2023
}%0 Journal Article
%1 Poker_2023
%A Poker, Yvonne
%A von Hardenberg, Sandra
%A Hofmann, Winfried
%A Tang, Ming
%A Baumann, Ulrich
%A Schwerk, Nicolaus
%A Wetzke, Martin
%A Lindenthal, Viola
%A Auber, Bernd
%A Schlegelberger, Brigitte
%A Ott, Hagen
%A von Bismarck, Philipp
%A Viemann, Dorothee
%A Dressler, Frank
%A Klemann, Christian
%A Bergmann, Anke Katharina
%D 2023
%I Frontiers Media {SA}
%J Frontiers in Genetics
%R 10.3389/fgene.2023.1065907
%T Systematic genetic analysis of pediatric patients with autoinflammatory diseases
%U https://doi.org/10.3389%2Ffgene.2023.1065907
%V 14
2022
- Biedenkapp, A., Speck, D., Sievers, S., Hutter, F., Lindauer, M., and Seipp, J. (2022)Learning Domain-Independent Policies for Open List Selection. In Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), pp. 1–9.
@inproceedings{BieSpe2022,
author = {Biedenkapp, André and Speck, David and Sievers, Silvan and Hutter, Frank and Lindauer, Marius and Seipp, Jendrik},
booktitle = {Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)},
keywords = {leibnizailab},
pages = {1-9},
title = {Learning Domain-Independent Policies for Open List Selection},
year = 2022
}%0 Conference Paper
%1 BieSpe2022
%A Biedenkapp, André
%A Speck, David
%A Sievers, Silvan
%A Hutter, Frank
%A Lindauer, Marius
%A Seipp, Jendrik
%B Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)
%D 2022
%P 1-9
%T Learning Domain-Independent Policies for Open List Selection - Adriaensen, S., Biedenkapp, A., Shala, G., Awad, N., Eimer, T., Lindauer, M., and Hutter, F. (2022)Automated Dynamic Algorithm Configuration, Journal of Artificial Intelligence Research, Morgan Kaufmann Publishers, Inc.The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there is still a lot of untapped potential as the learned configuration is static, i.e., parameter settings remain fixed throughout the run. However, it has been shown that some algorithm parameters are best adjusted dynamically during execution, e.g., to adapt to the current part of the optimization landscape. Thus far, this is most commonly achieved through hand-crafted heuristics. A promising recent alternative is to automatically learn such dynamic parameter adaptation policies from data. In this article, we give the first comprehensive account of this new field of automated dynamic algorithm configuration (DAC), present a series of recent advances, and provide a solid foundation for future research in this field. Specifically, we (i) situate DAC in the broader historical context of AI research; (ii) formalize DAC as a computational problem; (iii) identify the methods used in prior-art to tackle this problem; (iv) conduct empirical case studies for using DAC in evolutionary optimization, AI planning, and machine learning.
@article{2872944e4e864ceeb0fb4ba913b231b8,
abstract = {The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there is still a lot of untapped potential as the learned configuration is static, i.e., parameter settings remain fixed throughout the run. However, it has been shown that some algorithm parameters are best adjusted dynamically during execution, e.g., to adapt to the current part of the optimization landscape. Thus far, this is most commonly achieved through hand-crafted heuristics. A promising recent alternative is to automatically learn such dynamic parameter adaptation policies from data. In this article, we give the first comprehensive account of this new field of automated dynamic algorithm configuration (DAC), present a series of recent advances, and provide a solid foundation for future research in this field. Specifically, we (i) situate DAC in the broader historical context of AI research; (ii) formalize DAC as a computational problem; (iii) identify the methods used in prior-art to tackle this problem; (iv) conduct empirical case studies for using DAC in evolutionary optimization, AI planning, and machine learning.},
author = {Adriaensen, Steven and Biedenkapp, Andr{é} and Shala, Gresa and Awad, Noor and Eimer, Theresa and Lindauer, Marius and Hutter, Frank},
journal = {Journal of Artificial Intelligence Research},
keywords = {leibnizailab},
month = {05},
publisher = {Morgan Kaufmann Publishers, Inc.},
title = {Automated Dynamic Algorithm Configuration},
year = 2022
}%0 Journal Article
%1 2872944e4e864ceeb0fb4ba913b231b8
%A Adriaensen, Steven
%A Biedenkapp, Andr{é}
%A Shala, Gresa
%A Awad, Noor
%A Eimer, Theresa
%A Lindauer, Marius
%A Hutter, Frank
%D 2022
%I Morgan Kaufmann Publishers, Inc.
%J Journal of Artificial Intelligence Research
%T Automated Dynamic Algorithm Configuration
%X The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there is still a lot of untapped potential as the learned configuration is static, i.e., parameter settings remain fixed throughout the run. However, it has been shown that some algorithm parameters are best adjusted dynamically during execution, e.g., to adapt to the current part of the optimization landscape. Thus far, this is most commonly achieved through hand-crafted heuristics. A promising recent alternative is to automatically learn such dynamic parameter adaptation policies from data. In this article, we give the first comprehensive account of this new field of automated dynamic algorithm configuration (DAC), present a series of recent advances, and provide a solid foundation for future research in this field. Specifically, we (i) situate DAC in the broader historical context of AI research; (ii) formalize DAC as a computational problem; (iii) identify the methods used in prior-art to tackle this problem; (iv) conduct empirical case studies for using DAC in evolutionary optimization, AI planning, and machine learning. - Moosbauer, J., Casalicchio, G., Lindauer, M., and Bischl, B. (2022)Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution, arXiv.
@misc{https://doi.org/10.48550/arxiv.2206.05447,
author = {Moosbauer, Julia and Casalicchio, Giuseppe and Lindauer, Marius and Bischl, Bernd},
keywords = {leibnizailab},
publisher = {arXiv},
title = {Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution},
year = 2022
}%0 Generic
%1 https://doi.org/10.48550/arxiv.2206.05447
%A Moosbauer, Julia
%A Casalicchio, Giuseppe
%A Lindauer, Marius
%A Bischl, Bernd
%D 2022
%I arXiv
%R 10.48550/ARXIV.2206.05447
%T Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution
%U https://arxiv.org/abs/2206.05447 - Lange, A., K{ä}ding, M., Hinrichs, R., Ostermann, J., and Marx, S. (2022)Wire Break Detection in Bridge Tendons Using Low-Frequency Acoustic Emissions. In European Workshop on Structural Health Monitoring. EWSHM 2022..
@inproceedings{LanKae2022,
author = {Lange, Alexander and K{ä}ding, Max and Hinrichs, Reemt and Ostermann, J{ö}rn and Marx, Steffen},
booktitle = {European Workshop on Structural Health Monitoring. EWSHM 2022.},
keywords = {Wire},
month = {06},
title = {Wire Break Detection in Bridge Tendons Using Low-Frequency Acoustic Emissions},
year = 2022
}%0 Conference Paper
%1 LanKae2022
%A Lange, Alexander
%A K{ä}ding, Max
%A Hinrichs, Reemt
%A Ostermann, J{ö}rn
%A Marx, Steffen
%B European Workshop on Structural Health Monitoring. EWSHM 2022.
%D 2022
%R https://doi.org/10.1007/978-3-031-07322-9_104
%T Wire Break Detection in Bridge Tendons Using Low-Frequency Acoustic Emissions - Adhisantoso, Y. G., and Ostermann, J. (2022)Contact Matrix Compressor. In 2022 Data Compression Conference (DCC), pp. 399–408.
@inproceedings{AdhOst2022a,
author = {Adhisantoso, Yeremia Gunawan and Ostermann, J{ö}rn},
booktitle = {2022 Data Compression Conference (DCC)},
keywords = {leibnizailab},
month = {03},
pages = {399-408},
title = {Contact Matrix Compressor},
year = 2022
}%0 Conference Paper
%1 AdhOst2022a
%A Adhisantoso, Yeremia Gunawan
%A Ostermann, J{ö}rn
%B 2022 Data Compression Conference (DCC)
%D 2022
%P 399-408
%T Contact Matrix Compressor
%U https://ieeexplore.ieee.org/document/9810686 - Biswas, A., Patro, G. K., Ganguly, N., Gummadi, K. P., and Chakraborty, A. (2022)Toward Fair Recommendation in Two-sided Platforms, {ACM} Transactions on the Web, Association for Computing Machinery ({ACM}) 16, 1–34.
@article{Biswas_2022,
author = {Biswas, Arpita and Patro, Gourab K. and Ganguly, Niloy and Gummadi, Krishna P. and Chakraborty, Abhijnan},
journal = {{ACM} Transactions on the Web},
keywords = {"sys:relevantfor:l3s"},
month = {05},
number = 2,
pages = {1--34},
publisher = {Association for Computing Machinery ({ACM})},
title = {Toward Fair Recommendation in Two-sided Platforms},
volume = 16,
year = 2022
}%0 Journal Article
%1 Biswas_2022
%A Biswas, Arpita
%A Patro, Gourab K.
%A Ganguly, Niloy
%A Gummadi, Krishna P.
%A Chakraborty, Abhijnan
%D 2022
%I Association for Computing Machinery ({ACM})
%J {ACM} Transactions on the Web
%N 2
%P 1--34
%R 10.1145/3503624
%T Toward Fair Recommendation in Two-sided Platforms
%U https://doi.org/10.1145%2F3503624
%V 16 - Ren, Z., Nguyen, T. T., and Nejdl, W. (2022)Prototype learning for interpretable respiratory sound analysis. In {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2022, Virtual and Singapore, 23-27 May 2022, pp. 9087–9091, {IEEE}.
@inproceedings{DBLP:conf/icassp/RenNN22,
author = {Ren, Zhao and Nguyen, Thanh Tam and Nejdl, Wolfgang},
booktitle = {{IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2022, Virtual and Singapore, 23-27 May 2022},
keywords = {leibnizailab},
pages = {9087--9091},
publisher = {{IEEE}},
title = {Prototype learning for interpretable respiratory sound analysis},
year = 2022
}%0 Conference Paper
%1 DBLP:conf/icassp/RenNN22
%A Ren, Zhao
%A Nguyen, Thanh Tam
%A Nejdl, Wolfgang
%B {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2022, Virtual and Singapore, 23-27 May 2022
%D 2022
%I {IEEE}
%P 9087--9091
%R 10.1109/ICASSP43922.2022.9747014
%T Prototype learning for interpretable respiratory sound analysis
%U https://doi.org/10.1109/ICASSP43922.2022.9747014 - Ren, Z., Chang, Y., Nejdl, W., and Schuller, B. W. (2022)Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition, Acta Acustica 6, 1–5.
@article{renlearning,
author = {Ren, Zhao and Chang, Yi and Nejdl, Wolfgang and Schuller, Björn W.},
journal = {Acta Acustica},
keywords = {leibnizailab},
number = 29,
pages = {1-5},
title = {Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition},
volume = 6,
year = 2022
}%0 Journal Article
%1 renlearning
%A Ren, Zhao
%A Chang, Yi
%A Nejdl, Wolfgang
%A Schuller, Björn W.
%D 2022
%J Acta Acustica
%N 29
%P 1-5
%R https://doi.org/10.1051/aacus/2022029
%T Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition
%V 6 - Bondarenko, A., Fr{ö}be, M., Kiesel, J., Syed, S., Gurcke, T., Beloucif, M., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., and Hagen, M. (2022)Overview of Touch{é} 2022: Argument Retrieval: Argument Retrieval: Extended Abstract. In Advances in Information Retrieval (Hagen, M., Verberne, S., Macdonald, C., Seifert, C., Balog, K., N{\o}rv{\aa}g, K., and Setty, V., Eds.) Part 2., pp. 339–346, Springer Science and Business Media Deutschland GmbH, Germany.The goal of the Touch{é} lab on argument retrieval is to foster and support the development of technologies for argument mining and argument analysis. In the third edition of Touch{é}, we organize three shared tasks: (a) argument retrieval for controversial topics, where participants retrieve a gist of arguments from a collection of online debates, (b) argument retrieval for comparative questions, where participants retrieve argumentative passages from a generic web crawl, and (c) image retrieval for arguments, where participants retrieve images from a focused web crawl that show support or opposition to some stance. In this paper, we briefly summarize the results of two years of organizing Touch{é} and describe the planned setup for the third edition at CLEF 2022.
@inproceedings{8abf096d9cf44281978d6734f29a918e,
abstract = {The goal of the Touch{é} lab on argument retrieval is to foster and support the development of technologies for argument mining and argument analysis. In the third edition of Touch{é}, we organize three shared tasks: (a) argument retrieval for controversial topics, where participants retrieve a gist of arguments from a collection of online debates, (b) argument retrieval for comparative questions, where participants retrieve argumentative passages from a generic web crawl, and (c) image retrieval for arguments, where participants retrieve images from a focused web crawl that show support or opposition to some stance. In this paper, we briefly summarize the results of two years of organizing Touch{é} and describe the planned setup for the third edition at CLEF 2022.},
address = {Germany},
author = {Bondarenko, Alexander and Fr{ö}be, Maik and Kiesel, Johannes and Syed, Shahbaz and Gurcke, Timon and Beloucif, Meriem and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and Potthast, Martin and Hagen, Matthias},
booktitle = {Advances in Information Retrieval},
edition = {Part 2},
editor = {Hagen, Matthias and Verberne, Suzan and Macdonald, Craig and Seifert, Christin and Balog, Krisztian and N{\o}rv{\aa}g, Kjetil and Setty, Vinay},
keywords = {leibnizailab},
month = {04},
note = {Funding Information: Acknowledgments. This work was partially supported by the Deutsche Forschungs-gemeinschaft (DFG) through the projects “ACQuA” and “ACQuA 2.0” (Answering Comparative Questions with Arguments; grants HA 5851/2-1, HA 5851/2-2, BI 1544/7-1, BI 1544/7-2) and “OASiS: Objective Argument Summarization in Search” (grant WA 4591/3-1), all part of the priority program “RATIO: Robust Argumentation Machines” (SPP 1999), and the German Ministry for Science and Education (BMBF) through the project “Shared Tasks as an Innovative Approach to Implement AI and Big Data-based Applications within Universities (SharKI)” (grant FKZ 16DHB4021). We are also grateful to Jan Heinrich Reimer for developing the TARGER Python library.; 44th European Conference on Information Retrieval, ECIR 2022 ; Conference date: 10-04-2022 Through 14-04-2022},
pages = {339--346},
publisher = {Springer Science and Business Media Deutschland GmbH},
series = {Lecture Notes in Computer Science},
title = {Overview of Touch{é} 2022: Argument Retrieval: Argument Retrieval: Extended Abstract},
year = 2022
}%0 Conference Paper
%1 8abf096d9cf44281978d6734f29a918e
%A Bondarenko, Alexander
%A Fr{ö}be, Maik
%A Kiesel, Johannes
%A Syed, Shahbaz
%A Gurcke, Timon
%A Beloucif, Meriem
%A Panchenko, Alexander
%A Biemann, Chris
%A Stein, Benno
%A Wachsmuth, Henning
%A Potthast, Martin
%A Hagen, Matthias
%B Advances in Information Retrieval
%C Germany
%D 2022
%E Hagen, Matthias
%E Verberne, Suzan
%E Macdonald, Craig
%E Seifert, Christin
%E Balog, Krisztian
%E N{\o}rv{\aa}g, Kjetil
%E Setty, Vinay
%I Springer Science and Business Media Deutschland GmbH
%P 339--346
%R 10.1007/978-3-030-99739-7_43
%T Overview of Touch{é} 2022: Argument Retrieval: Argument Retrieval: Extended Abstract
%X The goal of the Touch{é} lab on argument retrieval is to foster and support the development of technologies for argument mining and argument analysis. In the third edition of Touch{é}, we organize three shared tasks: (a) argument retrieval for controversial topics, where participants retrieve a gist of arguments from a collection of online debates, (b) argument retrieval for comparative questions, where participants retrieve argumentative passages from a generic web crawl, and (c) image retrieval for arguments, where participants retrieve images from a focused web crawl that show support or opposition to some stance. In this paper, we briefly summarize the results of two years of organizing Touch{é} and describe the planned setup for the third edition at CLEF 2022.
%7 Part 2
%@ 9783030997380 - Dockhorn, A., and Kruse, R. (2022)State and Action Abstraction for Search and Reinforcement Learning Algorithms, pp. 1–18, Springer International Publishing.
@book{DocKru2022a,
author = {Dockhorn, Alexander and Kruse, Rudolf},
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%T State and Action Abstraction for Search and Reinforcement Learning Algorithms - Deng, D., and Lindauer, M. (2022)Searching in the Forest for Local Bayesian Optimization. In ECML/PKDD workshop on Meta-learning.Because of its sample efficiency, Bayesian optimization (BO) has become a popular approach dealing with expensive black-box optimization problems, such as hyperparameter optimization (HPO). Recent empirical experiments showed that the loss landscapes of HPO problems tend to be more benign than previously assumed, i.e. in the best case uni-modal and convex, such that a BO framework could be more efficient if it can focus on those promising local regions. In this paper, we propose BOinG, a two-stage approach that is tailored toward mid-sized configuration spaces, as one encounters in many HPO problems. In the first stage, we build a scalable global surrogate model with a random forest to describe the overall landscape structure. Further, we choose a promising subregion via a bottom-up approach on the upper-level tree structure. In the second stage, a local model in this subregion is utilized to suggest the point to be evaluated next. Empirical experiments show that BOinG is able to exploit the structure of typical HPO problems and performs particularly well on mid-sized problems from synthetic functions and HPO.
@inproceedings{94a049c1c1984b17af4b3933dbf8d511,
abstract = {Because of its sample efficiency, Bayesian optimization (BO) has become a popular approach dealing with expensive black-box optimization problems, such as hyperparameter optimization (HPO). Recent empirical experiments showed that the loss landscapes of HPO problems tend to be more benign than previously assumed, i.e. in the best case uni-modal and convex, such that a BO framework could be more efficient if it can focus on those promising local regions. In this paper, we propose BOinG, a two-stage approach that is tailored toward mid-sized configuration spaces, as one encounters in many HPO problems. In the first stage, we build a scalable global surrogate model with a random forest to describe the overall landscape structure. Further, we choose a promising subregion via a bottom-up approach on the upper-level tree structure. In the second stage, a local model in this subregion is utilized to suggest the point to be evaluated next. Empirical experiments show that BOinG is able to exploit the structure of typical HPO problems and performs particularly well on mid-sized problems from synthetic functions and HPO.},
author = {Deng, Difan and Lindauer, Marius},
booktitle = {ECML/PKDD workshop on Meta-learning},
keywords = {leibnizailab},
title = {Searching in the Forest for Local Bayesian Optimization},
year = 2022
}%0 Conference Paper
%1 94a049c1c1984b17af4b3933dbf8d511
%A Deng, Difan
%A Lindauer, Marius
%B ECML/PKDD workshop on Meta-learning
%D 2022
%T Searching in the Forest for Local Bayesian Optimization
%X Because of its sample efficiency, Bayesian optimization (BO) has become a popular approach dealing with expensive black-box optimization problems, such as hyperparameter optimization (HPO). Recent empirical experiments showed that the loss landscapes of HPO problems tend to be more benign than previously assumed, i.e. in the best case uni-modal and convex, such that a BO framework could be more efficient if it can focus on those promising local regions. In this paper, we propose BOinG, a two-stage approach that is tailored toward mid-sized configuration spaces, as one encounters in many HPO problems. In the first stage, we build a scalable global surrogate model with a random forest to describe the overall landscape structure. Further, we choose a promising subregion via a bottom-up approach on the upper-level tree structure. In the second stage, a local model in this subregion is utilized to suggest the point to be evaluated next. Empirical experiments show that BOinG is able to exploit the structure of typical HPO problems and performs particularly well on mid-sized problems from synthetic functions and HPO. - Benjamins, C., Jankovic, A., Raponi, E., Blom, {Koen van der}, Lindauer, M., and Doerr, C. (2022)Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. In 6th Workshop on Meta-Learning at NeurIPS 2022.Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used to optimize the AF, etc. We demonstrate in this work that a dynamic selection of the AF can benefit the BO design. More precisely, we show that already a na\{"}ive random forest regression model, built on top of exploratory landscape analysis features that are computed from the initial design points, suffices to recommend AFs that outperform any static choice, when considering performance over the classic BBOB benchmark suite for derivative-free numerical optimization methods on the COCO platform. Our work hence paves a way towards AutoML-assisted, on-the-fly BO designs that adjust their behavior on a run-by-run basis.
@inproceedings{aa885b1e4bee446e94b3f7e6350f33b7,
abstract = {Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used to optimize the AF, etc. We demonstrate in this work that a dynamic selection of the AF can benefit the BO design. More precisely, we show that already a na\{"}ive random forest regression model, built on top of exploratory landscape analysis features that are computed from the initial design points, suffices to recommend AFs that outperform any static choice, when considering performance over the classic BBOB benchmark suite for derivative-free numerical optimization methods on the COCO platform. Our work hence paves a way towards AutoML-assisted, on-the-fly BO designs that adjust their behavior on a run-by-run basis.},
author = {Benjamins, Carolin and Jankovic, Anja and Raponi, Elena and Blom, {Koen van der} and Lindauer, Marius and Doerr, Carola},
booktitle = {6th Workshop on Meta-Learning at NeurIPS 2022},
keywords = {leibnizailab},
month = 11,
title = {Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis},
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}%0 Conference Paper
%1 aa885b1e4bee446e94b3f7e6350f33b7
%A Benjamins, Carolin
%A Jankovic, Anja
%A Raponi, Elena
%A Blom, {Koen van der}
%A Lindauer, Marius
%A Doerr, Carola
%B 6th Workshop on Meta-Learning at NeurIPS 2022
%D 2022
%T Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
%X Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used to optimize the AF, etc. We demonstrate in this work that a dynamic selection of the AF can benefit the BO design. More precisely, we show that already a na\{"}ive random forest regression model, built on top of exploratory landscape analysis features that are computed from the initial design points, suffices to recommend AFs that outperform any static choice, when considering performance over the classic BBOB benchmark suite for derivative-free numerical optimization methods on the COCO platform. Our work hence paves a way towards AutoML-assisted, on-the-fly BO designs that adjust their behavior on a run-by-run basis. - Fehring, L., Hanselle, J., and Tornede, A. (2022)HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. In NeurIPS Workshop on Meta Learning (MetaLearn 2022).
@inproceedings{ac6176b1d3fa4aea9c3a3e6521490242,
author = {Fehring, Lukas and Hanselle, Jonas and Tornede, Alexander},
booktitle = {NeurIPS Workshop on Meta Learning (MetaLearn 2022)},
keywords = {leibnizailab},
title = {HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection},
year = 2022
}%0 Conference Paper
%1 ac6176b1d3fa4aea9c3a3e6521490242
%A Fehring, Lukas
%A Hanselle, Jonas
%A Tornede, Alexander
%B NeurIPS Workshop on Meta Learning (MetaLearn 2022)
%D 2022
%T HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection - Xuan, Q. L., Adhisantoso, Y. G., Munderloh, M., and Ostermann, J. (2022)Uncertainty-Aware Remaining Useful Life Prediction for Predictive Maintenance Using Deep Learning (accepted). In 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME.
@inproceedings{LeXAdh2022a,
author = {Xuan, Quy Le and Adhisantoso, Yeremia Gunawan and Munderloh, Marco and Ostermann, J{ö}rn},
booktitle = {16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME},
keywords = {Uncertainty-Aware},
title = {Uncertainty-Aware Remaining Useful Life Prediction for Predictive Maintenance Using Deep Learning (accepted)},
year = 2022
}%0 Conference Paper
%1 LeXAdh2022a
%A Xuan, Quy Le
%A Adhisantoso, Yeremia Gunawan
%A Munderloh, Marco
%A Ostermann, J{ö}rn
%B 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME
%D 2022
%T Uncertainty-Aware Remaining Useful Life Prediction for Predictive Maintenance Using Deep Learning (accepted) - Dong, N., Mücke, S., and Khosla, M. (2022)MuCoMiD: A Multitask Graph Convolutional Learning Framework for miRNA-Disease Association Prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics 19, 3081–3092.
@article{9779549,
author = {Dong, Ngan and Mücke, Stefanie and Khosla, Megha},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
keywords = {leibnizailab},
number = 6,
pages = {3081-3092},
title = {MuCoMiD: A Multitask Graph Convolutional Learning Framework for miRNA-Disease Association Prediction},
volume = 19,
year = 2022
}%0 Journal Article
%1 9779549
%A Dong, Ngan
%A Mücke, Stefanie
%A Khosla, Megha
%D 2022
%J IEEE/ACM Transactions on Computational Biology and Bioinformatics
%N 6
%P 3081-3092
%R 10.1109/TCBB.2022.3176456
%T MuCoMiD: A Multitask Graph Convolutional Learning Framework for miRNA-Disease Association Prediction
%V 19 - Poker, Y., Hardenberg, S. V., Hofmann, W., Tang, M., Baumann, U., Schwerk, N., Wetzke, M., Lindenthal, V., Auber, B., Schlegelberger, B., Ott, H., Bismarck, P. V., Viemann, B., Dressler, F., Klemann, C., and Bergmann, A. K. (2022)Genetics in inborn errors of immunity: pediatric auto inflammatory phenotypes and the underlying genetic causes in 125 families. In .
@conference{poker2022ESHG,
author = {Poker, Yvonne and Hardenberg, Sandra Von and Hofmann, Winfried and Tang, Michelle and Baumann, Ulrich and Schwerk, Nicolaus and Wetzke, Martin and Lindenthal, Viola and Auber, Bernd and Schlegelberger, Brigitte and Ott, H and Bismarck, Philipp Von and Viemann, Borothee and Dressler, Frank and Klemann, Christian and Bergmann, Anke Katharina},
keywords = {l3s},
title = {Genetics in inborn errors of immunity: pediatric auto inflammatory phenotypes and the underlying genetic causes in 125 families},
year = 2022
}%0 Generic
%1 poker2022ESHG
%A Poker, Yvonne
%A Hardenberg, Sandra Von
%A Hofmann, Winfried
%A Tang, Michelle
%A Baumann, Ulrich
%A Schwerk, Nicolaus
%A Wetzke, Martin
%A Lindenthal, Viola
%A Auber, Bernd
%A Schlegelberger, Brigitte
%A Ott, H
%A Bismarck, Philipp Von
%A Viemann, Borothee
%A Dressler, Frank
%A Klemann, Christian
%A Bergmann, Anke Katharina
%D 2022
%T Genetics in inborn errors of immunity: pediatric auto inflammatory phenotypes and the underlying genetic causes in 125 families - Pandey, P. K., Adhikari, B., Mazumdar, M., and Ganguly, N. (2022)Modeling Signed Networks as 2-Layer Growing Networks, IEEE Transactions on Knowledge and Data Engineering 34, 3377–3390.We propose modeling signed networks by considering two layers in a social network for generation of positive and negative links where both the layers comprise of identical set of nodes. The growth process is modeled based on preferential attachment, formation of links probabilistically asserting structural balance of local groups, and internal growth which happens without addition of new nodes. We prove that the degree distribution of a generated network follows a power-law whose exponent depends on the largest eigenvalue of a matrix which governs the dynamics of growth of degrees of nodes with respect to positive and negative links. A computable formula for average degree and lower-bounds for the number of balanced and unbalanced triads of modelled networks are also obtained. A method for structural reconstruction of real signed networks is formulated through estimation the values of the model parameters to generate the network that can inherit different structural properties of the corresponding real network. Experimental results show that our model which we term as 2L-SNM can replicate properties of several real world signed networks much more robustly than competitive state-of-the-art techniques.
@article{9200743,
abstract = {We propose modeling signed networks by considering two layers in a social network for generation of positive and negative links where both the layers comprise of identical set of nodes. The growth process is modeled based on preferential attachment, formation of links probabilistically asserting structural balance of local groups, and internal growth which happens without addition of new nodes. We prove that the degree distribution of a generated network follows a power-law whose exponent depends on the largest eigenvalue of a matrix which governs the dynamics of growth of degrees of nodes with respect to positive and negative links. A computable formula for average degree and lower-bounds for the number of balanced and unbalanced triads of modelled networks are also obtained. A method for structural reconstruction of real signed networks is formulated through estimation the values of the model parameters to generate the network that can inherit different structural properties of the corresponding real network. Experimental results show that our model which we term as 2L-SNM can replicate properties of several real world signed networks much more robustly than competitive state-of-the-art techniques.},
author = {Pandey, Pradumn Kumar and Adhikari, Bibhas and Mazumdar, Mainak and Ganguly, Niloy},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {"sys:relevantfor:l3s"},
month = {07},
number = 7,
pages = {3377-3390},
title = {Modeling Signed Networks as 2-Layer Growing Networks},
volume = 34,
year = 2022
}%0 Journal Article
%1 9200743
%A Pandey, Pradumn Kumar
%A Adhikari, Bibhas
%A Mazumdar, Mainak
%A Ganguly, Niloy
%D 2022
%J IEEE Transactions on Knowledge and Data Engineering
%N 7
%P 3377-3390
%R 10.1109/TKDE.2020.3024779
%T Modeling Signed Networks as 2-Layer Growing Networks
%U https://ieeexplore.ieee.org/document/9200743/
%V 34
%X We propose modeling signed networks by considering two layers in a social network for generation of positive and negative links where both the layers comprise of identical set of nodes. The growth process is modeled based on preferential attachment, formation of links probabilistically asserting structural balance of local groups, and internal growth which happens without addition of new nodes. We prove that the degree distribution of a generated network follows a power-law whose exponent depends on the largest eigenvalue of a matrix which governs the dynamics of growth of degrees of nodes with respect to positive and negative links. A computable formula for average degree and lower-bounds for the number of balanced and unbalanced triads of modelled networks are also obtained. A method for structural reconstruction of real signed networks is formulated through estimation the values of the model parameters to generate the network that can inherit different structural properties of the corresponding real network. Experimental results show that our model which we term as 2L-SNM can replicate properties of several real world signed networks much more robustly than competitive state-of-the-art techniques. - Ren, Z., Qian, K., Dong, F., Dai, Z., Nejdl, W., Yamamoto, Y., and Schuller, B. W. (2022)Deep attention-based neural networks for explainable heart sound classification, Machine Learning with Applications 9, 1–9.
@article{ren2022attentionbased,
author = {Ren, Zhao and Qian, Kun and Dong, Fengquan and Dai, Zhenyu and Nejdl, Wolfgang and Yamamoto, Yoshiharu and Schuller, Björn W.},
journal = {Machine Learning with Applications},
keywords = {leibnizailab},
pages = {1-9},
title = {Deep attention-based neural networks for explainable heart sound classification},
volume = 9,
year = 2022
}%0 Journal Article
%1 ren2022attentionbased
%A Ren, Zhao
%A Qian, Kun
%A Dong, Fengquan
%A Dai, Zhenyu
%A Nejdl, Wolfgang
%A Yamamoto, Yoshiharu
%A Schuller, Björn W.
%D 2022
%J Machine Learning with Applications
%P 1-9
%R https://doi.org/10.1016/j.mlwa.2022.100322
%T Deep attention-based neural networks for explainable heart sound classification
%V 9 - Mullick, A., Purkayastha, S., Goyal, P., and Ganguly, N. (2022)A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection. In Findings of the Association for Computational Linguistics: {NAACL} 2022, Association for Computational Linguistics.
@inproceedings{Mullick_2022,
author = {Mullick, Ankan and Purkayastha, Sukannya and Goyal, Pawan and Ganguly, Niloy},
booktitle = {Findings of the Association for Computational Linguistics: {NAACL} 2022},
keywords = {"sys:relevantfor:l3s"},
publisher = {Association for Computational Linguistics},
title = {A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection},
year = 2022
}%0 Conference Paper
%1 Mullick_2022
%A Mullick, Ankan
%A Purkayastha, Sukannya
%A Goyal, Pawan
%A Ganguly, Niloy
%B Findings of the Association for Computational Linguistics: {NAACL} 2022
%D 2022
%I Association for Computational Linguistics
%R 10.18653/v1/2022.findings-naacl.21
%T A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection
%U https://doi.org/10.18653%2Fv1%2F2022.findings-naacl.21 - Hinrichs, R., Gerkens, K., Lange, A., and Ostermann, J. (2022)Classification of Guitar Effects and Extraction of their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks. In EvoMUSART 2022.
@inproceedings{HinGer2022,
author = {Hinrichs, Reemt and Gerkens, Kevin and Lange, Alexander and Ostermann, J{ö}rn},
booktitle = {EvoMUSART 2022},
keywords = {Extraction},
title = {Classification of Guitar Effects and Extraction of their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks},
year = 2022
}%0 Conference Paper
%1 HinGer2022
%A Hinrichs, Reemt
%A Gerkens, Kevin
%A Lange, Alexander
%A Ostermann, J{ö}rn
%B EvoMUSART 2022
%D 2022
%T Classification of Guitar Effects and Extraction of their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks - Poddar, S., Mondal, M., Misra, J., Ganguly, N., and Ghosh, S. (2022)Winds of Change: Impact of {COVID}-19 on Vaccine-Related Opinions of Twitter Users, Proceedings of the International {AAAI} Conference on Web and Social Media, Association for the Advancement of Artificial Intelligence ({AAAI}) 16, 782–793.
@article{Poddar_2022,
author = {Poddar, Soham and Mondal, Mainack and Misra, Janardan and Ganguly, Niloy and Ghosh, Saptarshi},
journal = {Proceedings of the International {AAAI} Conference on Web and Social Media},
keywords = {"sys:relevantfor:l3s"},
month = {05},
pages = {782--793},
publisher = {Association for the Advancement of Artificial Intelligence ({AAAI})},
title = {Winds of Change: Impact of {COVID}-19 on Vaccine-Related Opinions of Twitter Users},
volume = 16,
year = 2022
}%0 Journal Article
%1 Poddar_2022
%A Poddar, Soham
%A Mondal, Mainack
%A Misra, Janardan
%A Ganguly, Niloy
%A Ghosh, Saptarshi
%D 2022
%I Association for the Advancement of Artificial Intelligence ({AAAI})
%J Proceedings of the International {AAAI} Conference on Web and Social Media
%P 782--793
%R 10.1609/icwsm.v16i1.19334
%T Winds of Change: Impact of {COVID}-19 on Vaccine-Related Opinions of Twitter Users
%U https://doi.org/10.1609%2Ficwsm.v16i1.19334
%V 16 - Benjak, M., Aust, N., Samayoa, Y., and Ostermann, J. (2022)Neural Network-based Error Concealment for B-Frames in VVC. In 2022 IEEE International Symposium on Circuits and Systems (ISCAS).
@inproceedings{BenAus2022a,
author = {Benjak, Martin and Aust, Niklas and Samayoa, Yasser and Ostermann, J{ö}rn},
booktitle = {2022 IEEE International Symposium on Circuits and Systems (ISCAS)},
keywords = {Neural},
title = {Neural Network-based Error Concealment for B-Frames in VVC},
year = 2022
}%0 Conference Paper
%1 BenAus2022a
%A Benjak, Martin
%A Aust, Niklas
%A Samayoa, Yasser
%A Ostermann, J{ö}rn
%B 2022 IEEE International Symposium on Circuits and Systems (ISCAS)
%D 2022
%T Neural Network-based Error Concealment for B-Frames in VVC - Awiszus, M., Schubert, F., and Rosenhahn, B. (2022)Wor(l)d-GAN: Towards Natural Language Based PCG in Minecraft, IEEE Transactions on Games.
@article{AwiSch2022,
author = {Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
journal = {IEEE Transactions on Games},
keywords = {Based},
month = {02},
note = {11 pages, 10 figures.},
title = {Wor(l)d-GAN: Towards Natural Language Based PCG in Minecraft},
year = 2022
}%0 Journal Article
%1 AwiSch2022
%A Awiszus, Maren
%A Schubert, Frederik
%A Rosenhahn, Bodo
%D 2022
%J IEEE Transactions on Games
%T Wor(l)d-GAN: Towards Natural Language Based PCG in Minecraft - Hvarfner, C., Stoll, D., Souza, A., Nardi, L., Lindauer, M., and Hutter, F. (2022)piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. In 10th International Conference on Learning Representations, ICLR’22, pp. 1–30.
@inproceedings{HvaSto2022a,
author = {Hvarfner, Carl and Stoll, Danny and Souza, Artur and Nardi, Luigi and Lindauer, Marius and Hutter, Frank},
booktitle = {10th International Conference on Learning Representations, ICLR'22},
keywords = {Acquisition},
month = {04},
pages = {1-30},
title = {piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization},
year = 2022
}%0 Conference Paper
%1 HvaSto2022a
%A Hvarfner, Carl
%A Stoll, Danny
%A Souza, Artur
%A Nardi, Luigi
%A Lindauer, Marius
%A Hutter, Frank
%B 10th International Conference on Learning Representations, ICLR'22
%D 2022
%P 1-30
%T piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
%U https://openreview.net/pdf/1ce81b811a1cac6ed2405793a93e8512b1b50005.pdf - Dong, T. N., Schrader, J., M{ü}cke, S., and Khosla, M. (2022)A Message Passing framework with Multiple data integration for miRNA-Disease association prediction, Scientific Reports 12, 16259.Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach’s superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption.
@article{dong2022message,
abstract = {Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach’s superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption.},
author = {Dong, Thi Ngan and Schrader, Johanna and M{ü}cke, Stefanie and Khosla, Megha},
journal = {Scientific Reports},
keywords = {l3s},
month = {09},
pages = 16259,
title = {A Message Passing framework with Multiple data integration for miRNA-Disease association prediction},
volume = 12,
year = 2022
}%0 Journal Article
%1 dong2022message
%A Dong, Thi Ngan
%A Schrader, Johanna
%A M{ü}cke, Stefanie
%A Khosla, Megha
%D 2022
%J Scientific Reports
%P 16259
%R 10.1038/s41598-022-20529-5
%T A Message Passing framework with Multiple data integration for miRNA-Disease association prediction
%U https://www.nature.com/articles/s41598-022-20529-5
%V 12
%X Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach’s superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption. - Voges, J. (2022)Compression of DNA Sequencing Data, Fortschritt-Berichte VDI.
@article{Vog2022a,
author = {Voges, Jan},
journal = {Fortschritt-Berichte VDI},
keywords = {DNA},
note = {https://doi.org/10.15488/12422 https://doi.org/10.51202/9783186878106-I https://www.vdi-nachrichten.com/shop/compression-of-dna-sequencing-data/ https://elibrary.vdi-verlag.de/10.51202/9783186878106-I/ ISBN print: 978-3-18-387810-9 ISBN online: 978-3-18-687810-6},
title = {Compression of DNA Sequencing Data},
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%J Fortschritt-Berichte VDI
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%U https://doi.org/10.51202/9783186878106-I
%@ 978-3-18-387810-9 - Kellermann, C., Neumann, E., and Ostermann, J. (2022)Prediction of variable forecast horizons with artificial neural networks by embedding the temporal resolution warping. In International Conference on Control, Automation and Diagnosis (ICCAD), pp. 1–5.
@inproceedings{KelNeu2022,
author = {Kellermann, Christoph and Neumann, Erik and Ostermann, J{ö}rn},
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title = {Prediction of variable forecast horizons with artificial neural networks by embedding the temporal resolution warping},
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%R 10.1109/ICCAD55197.2022.9853884
%T Prediction of variable forecast horizons with artificial neural networks by embedding the temporal resolution warping - Hinrichs, R., Ortmann, F., and Ostermann, J. (2022)Vector-Quantized Zero-Delay Deep Autoencoders for the Compression of Electrical Stimulation Patterns of Cochlear Implants Using STOI. In IEEE EMBS 2022.
@inproceedings{HinOrt2022,
author = {Hinrichs, Reemt and Ortmann, Felix and Ostermann, J{ö}rn},
booktitle = {IEEE EMBS 2022},
keywords = {Vector-Quantized},
title = {Vector-Quantized Zero-Delay Deep Autoencoders for the Compression of Electrical Stimulation Patterns of Cochlear Implants Using STOI},
year = 2022
}%0 Conference Paper
%1 HinOrt2022
%A Hinrichs, Reemt
%A Ortmann, Felix
%A Ostermann, J{ö}rn
%B IEEE EMBS 2022
%D 2022
%T Vector-Quantized Zero-Delay Deep Autoencoders for the Compression of Electrical Stimulation Patterns of Cochlear Implants Using STOI - Alshomary, M., Rieskamp, J., and Wachsmuth, H. (2022)Generating Contrastive Snippets for Argument Search. In Proceedings of the 9th International Conference on Computational Models of Argument, pp. 21–31.
@inproceedings{Alshomary_Rieskamp_Wachsmuth_2022,
author = {Alshomary, Milad and Rieskamp, Jonas and Wachsmuth, Henning},
booktitle = {Proceedings of the 9th International Conference on Computational Models of Argument},
keywords = {leibnizailab},
pages = {21–31},
title = {Generating Contrastive Snippets for Argument Search},
year = 2022
}%0 Conference Paper
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%A Alshomary, Milad
%A Rieskamp, Jonas
%A Wachsmuth, Henning
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%D 2022
%P 21–31
%T Generating Contrastive Snippets for Argument Search - Dong, T. N., Schrader, J., Mücke, S., and Khosla, M. (2022)A message passing framework with multiple data integration for miRNA-disease association prediction, Scientific Reports, Springer Science and Business Media LLC 12.
@article{Dong_2022,
author = {Dong, Thi Ngan and Schrader, Johanna and Mücke, Stefanie and Khosla, Megha},
journal = {Scientific Reports},
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%U http://dx.doi.org/10.1038/s41598-022-20529-5
%V 12 - Dockhorn, A. (2022)Choosing Representation, Mutation, and Crossover in Genetic Algorithms, IEEE Computational Intelligence Magazine 17, 52–53.
@article{Doc2022,
author = {Dockhorn, Alexander},
journal = {IEEE Computational Intelligence Magazine},
keywords = {Representation},
month = 11,
note = {This is an immersive article. Therefore, extended interactive ressources are provided at the publisher webpage. A pre-print can be found at: https://aiexplained.github.io/},
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%T Choosing Representation, Mutation, and Crossover in Genetic Algorithms
%U https://ieeexplore.ieee.org/document/9942691
%V 17 - Wolff, J., Klimke, A., Marschollek, M., and Kacprowski, T. (2022)Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data, Sci Rep 12, 15912.
@article{RN2,
author = {Wolff, J. and Klimke, A. and Marschollek, M. and Kacprowski, T.},
journal = {Sci Rep},
keywords = {l3s},
number = 1,
pages = 15912,
title = {Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data},
type = {Journal Article},
volume = 12,
year = 2022
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%A Wolff, J.
%A Klimke, A.
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%A Kacprowski, T.
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%R 10.1038/s41598-022-20190-y
%T Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data
%U https://www.ncbi.nlm.nih.gov/pubmed/36151267
%V 12 - Wagner, L., Olson, C., and Dockhorn, A. (2022)Generalizations of Steering - A Modular Design. In 2022 IEEE Conference on Games (CoG), pp. 1–4.
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author = {Wagner, Lars and Olson, Christopher and Dockhorn, Alexander},
booktitle = {2022 IEEE Conference on Games (CoG)},
keywords = {of},
pages = {1-4},
title = {Generalizations of Steering - A Modular Design},
year = 2022
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%A Wagner, Lars
%A Olson, Christopher
%A Dockhorn, Alexander
%B 2022 IEEE Conference on Games (CoG)
%D 2022
%P 1-4
%T Generalizations of Steering - A Modular Design - Rumberg, L., Gebauer, C., Ehlert, H., Wallbaum, M., Bornholt, L., Ostermann, J., and L{ü}dtke, U. (2022)kidsTALC: A Corpus of 3- to 11-year-old German Children’s Connected Natural Speech. In Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association.
@inproceedings{RumGeb2022a,
author = {Rumberg, Lars and Gebauer, Christopher and Ehlert, Hanna and Wallbaum, Maren and Bornholt, Lena and Ostermann, J{ö}rn and L{ü}dtke, Ulrike},
booktitle = {Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association},
keywords = {kidsTALC},
month = {09},
title = {kidsTALC: A Corpus of 3- to 11-year-old German Children’s Connected Natural Speech},
year = 2022
}%0 Conference Paper
%1 RumGeb2022a
%A Rumberg, Lars
%A Gebauer, Christopher
%A Ehlert, Hanna
%A Wallbaum, Maren
%A Bornholt, Lena
%A Ostermann, J{ö}rn
%A L{ü}dtke, Ulrike
%B Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association
%D 2022
%T kidsTALC: A Corpus of 3- to 11-year-old German Children’s Connected Natural Speech - Schäfer, J., Tang, M., Luu, D., Bergmann, A. K., and Wiese, L. (2022)Graph4Med: a web application and a graph database for visualizing and analyzing medical databases, BMC bioinformatics (Zandomeneghi, S., Ed.) 23.
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author = {Schäfer, Jero# and Tang, Ming# and Luu, Danny and Bergmann, Anke Katharina and Wiese, Lena},
editor = {Zandomeneghi, Sara},
journal = {BMC bioinformatics},
keywords = {l3s},
number = {537 (2022)},
title = {Graph4Med: a web application and a graph database for visualizing and analyzing medical databases},
volume = 23,
year = 2022
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%1 zandomeneghi2022graph4med
%A Schäfer, Jero#
%A Tang, Ming#
%A Luu, Danny
%A Bergmann, Anke Katharina
%A Wiese, Lena
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%E Zandomeneghi, Sara
%J BMC bioinformatics
%N 537 (2022)
%R https://doi.org/10.1186/s12859-022-05092-0
%T Graph4Med: a web application and a graph database for visualizing and analyzing medical databases
%V 23 - Cordes, K., Reinders, C., Hindricks, P., Lammers, J., Rosenhahn, B., and Broszio, H. (2022)RoadSaW: A Large-Scale Dataset for Camera-Based Road Surface and Wetness Estimation. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
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author = {Cordes, Kai and Reinders, Christoph and Hindricks, Paul and Lammers, Jonas and Rosenhahn, Bodo and Broszio, Hellward},
booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
keywords = {RoadSaW},
title = {RoadSaW: A Large-Scale Dataset for Camera-Based Road Surface and Wetness Estimation},
year = 2022
}%0 Conference Paper
%1 CorRei2022a
%A Cordes, Kai
%A Reinders, Christoph
%A Hindricks, Paul
%A Lammers, Jonas
%A Rosenhahn, Bodo
%A Broszio, Hellward
%B 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
%D 2022
%T RoadSaW: A Large-Scale Dataset for Camera-Based Road Surface and Wetness Estimation
%U https://roadsaw.viscoda.com - Hvarfner, C., Stoll, D., Souza, A., Lindauer, M., Hutter, F., and Nardi, L. (2022)πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. In 10th International Conference on Learning Representations, ICLR 2022, OpenReview.net.
@inproceedings{Hvarfner0000,
author = {Hvarfner, Carl and Stoll, Danny and Souza, Artur and Lindauer, Marius and Hutter, Frank and Nardi, Luigi},
booktitle = {10th International Conference on Learning Representations, ICLR 2022},
keywords = {leibnizailab},
publisher = {OpenReview.net},
title = {πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization},
year = 2022
}%0 Conference Paper
%1 Hvarfner0000
%A Hvarfner, Carl
%A Stoll, Danny
%A Souza, Artur
%A Lindauer, Marius
%A Hutter, Frank
%A Nardi, Luigi
%B 10th International Conference on Learning Representations, ICLR 2022
%D 2022
%I OpenReview.net
%T πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization - Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Ruhkopf, T., Sass, R., and Hutter, F. (2022)SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization, Journal of Machine Learning Research 23 (2022) 1–9.
@article{noauthororeditor,
author = {Lindauer, Marius and Eggensperger, Katharina and Feurer, Matthias and Biedenkapp, André and Deng, Difan and Benjamins, Carolin and Ruhkopf, Tim and Sass, René and Hutter, Frank},
journal = {Journal of Machine Learning Research 23 (2022)},
keywords = {leibnizailab},
pages = {1-9},
title = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
year = 2022
}%0 Journal Article
%1 noauthororeditor
%A Lindauer, Marius
%A Eggensperger, Katharina
%A Feurer, Matthias
%A Biedenkapp, André
%A Deng, Difan
%A Benjamins, Carolin
%A Ruhkopf, Tim
%A Sass, René
%A Hutter, Frank
%D 2022
%J Journal of Machine Learning Research 23 (2022)
%P 1-9
%T SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization - Wachsmuth, H., and Alshomary, M. (2022)“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning How to Explain. In Proceedings of the 29th International Conference on Computational Linguistics, pp. 344–354.
@inproceedings{Wachsmuth_Alshomary_2022,
author = {Wachsmuth, Henning and Alshomary, Milad},
booktitle = {Proceedings of the 29th International Conference on Computational Linguistics},
keywords = {leibnizailab},
pages = {344–354},
title = {“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning How to Explain},
year = 2022
}%0 Conference Paper
%1 Wachsmuth_Alshomary_2022
%A Wachsmuth, Henning
%A Alshomary, Milad
%B Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%P 344–354
%T “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning How to Explain - Bothmann, L., Strickroth, S., Casalicchio, G., Rügamer, D., Lindauer, M., Scheipl, F., and Bischl, B. (2022)Developing Open Source Educational Resources for Machine Learning and Data Science. In Teaching Machine Learning Workshop at ECML 2022.
@inproceedings{https://doi.org/10.48550/arxiv.2107.14330,
author = {Bothmann, Ludwig and Strickroth, Sven and Casalicchio, Giuseppe and Rügamer, David and Lindauer, Marius and Scheipl, Fabian and Bischl, Bernd},
booktitle = {Teaching Machine Learning Workshop at ECML 2022},
keywords = {leibnizailab},
title = {Developing Open Source Educational Resources for Machine Learning and Data Science},
year = 2022
}%0 Conference Paper
%1 https://doi.org/10.48550/arxiv.2107.14330
%A Bothmann, Ludwig
%A Strickroth, Sven
%A Casalicchio, Giuseppe
%A Rügamer, David
%A Lindauer, Marius
%A Scheipl, Fabian
%A Bischl, Bernd
%B Teaching Machine Learning Workshop at ECML 2022
%D 2022
%T Developing Open Source Educational Resources for Machine Learning and Data Science - Lauscher, A., Wachsmuth, H., Gurevych, I., and Glava{\v s}, G. (2022)Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation, Transactions of the Association for Computational Linguistics, MIT Press Journals 10, 1392–1422.Despite extensive research efforts in recent years, computational argumentation (CA) remains one of the most challenging areas of natural language processing. The reason for this is the inherent complexity of the cognitive processes behind human argumentation, which integrate a plethora of different types of knowledge, ranging from topic-specific facts and common sense to rhetorical knowledge. The integration of knowledge from such a wide range in CA requires modeling capabilities far beyond many other natural language understanding tasks. Existing research on mining, assessing, reasoning over, and generating arguments largely acknowledges that much more knowledge is needed to accurately model argumentation computationally. However, a systematic overview of the types of knowledge introduced in existing CA models is missing, hindering targeted progress in the field. Adopting the operational definition of knowledge as any task-relevant normative information not provided as input, the survey paper at hand fills this gap by (1) proposing a taxonomy of types of knowledge required in CA tasks, (2) systematizing the large body of CA work according to the reliance on and exploitation of these knowledge types for the four main research areas in CA, and (3) outlining and discussing directions for future research efforts in CA.
@article{26e974e3e354469ca8fd0586d43913ed,
abstract = {Despite extensive research efforts in recent years, computational argumentation (CA) remains one of the most challenging areas of natural language processing. The reason for this is the inherent complexity of the cognitive processes behind human argumentation, which integrate a plethora of different types of knowledge, ranging from topic-specific facts and common sense to rhetorical knowledge. The integration of knowledge from such a wide range in CA requires modeling capabilities far beyond many other natural language understanding tasks. Existing research on mining, assessing, reasoning over, and generating arguments largely acknowledges that much more knowledge is needed to accurately model argumentation computationally. However, a systematic overview of the types of knowledge introduced in existing CA models is missing, hindering targeted progress in the field. Adopting the operational definition of knowledge as any task-relevant normative information not provided as input, the survey paper at hand fills this gap by (1) proposing a taxonomy of types of knowledge required in CA tasks, (2) systematizing the large body of CA work according to the reliance on and exploitation of these knowledge types for the four main research areas in CA, and (3) outlining and discussing directions for future research efforts in CA.},
author = {Lauscher, Anne and Wachsmuth, Henning and Gurevych, Iryna and Glava{\v s}, Goran},
journal = {Transactions of the Association for Computational Linguistics},
keywords = {leibnizailab},
month = 12,
note = {Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.},
number = 10,
pages = {1392--1422},
publisher = {MIT Press Journals},
title = {Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation},
volume = 10,
year = 2022
}%0 Journal Article
%1 26e974e3e354469ca8fd0586d43913ed
%A Lauscher, Anne
%A Wachsmuth, Henning
%A Gurevych, Iryna
%A Glava{\v s}, Goran
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%I MIT Press Journals
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%N 10
%P 1392--1422
%R 10.1162/tacl_a_00525
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%V 10
%X Despite extensive research efforts in recent years, computational argumentation (CA) remains one of the most challenging areas of natural language processing. The reason for this is the inherent complexity of the cognitive processes behind human argumentation, which integrate a plethora of different types of knowledge, ranging from topic-specific facts and common sense to rhetorical knowledge. The integration of knowledge from such a wide range in CA requires modeling capabilities far beyond many other natural language understanding tasks. Existing research on mining, assessing, reasoning over, and generating arguments largely acknowledges that much more knowledge is needed to accurately model argumentation computationally. However, a systematic overview of the types of knowledge introduced in existing CA models is missing, hindering targeted progress in the field. Adopting the operational definition of knowledge as any task-relevant normative information not provided as input, the survey paper at hand fills this gap by (1) proposing a taxonomy of types of knowledge required in CA tasks, (2) systematizing the large body of CA work according to the reliance on and exploitation of these knowledge types for the four main research areas in CA, and (3) outlining and discussing directions for future research efforts in CA. - Kiesel, J., Alshomary, M., Handke, N., Cai, X., Wachsmuth, H., and Stein, B. (2022)Identifying the Human Values behind Arguments. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 4459–4471.
@inproceedings{Kiesel_Alshomary_Handke_Cai_Wachsmuth_Stein_2022,
author = {Kiesel, Johannes and Alshomary, Milad and Handke, Nicolas and Cai, Xiaoni and Wachsmuth, Henning and Stein, Benno},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
keywords = {leibnizailab},
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%A Alshomary, Milad
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%A Cai, Xiaoni
%A Wachsmuth, Henning
%A Stein, Benno
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%D 2022
%P 4459–4471
%T Identifying the Human Values behind Arguments - Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B., and Lindauer, M. (2022)POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning, Arxiv Preprint, arXiv.
@article{https://doi.org/10.48550/arxiv.2205.11357,
author = {Schubert, Frederik and Benjamins, Carolin and Döhler, Sebastian and Rosenhahn, Bodo and Lindauer, Marius},
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%U https://arxiv.org/abs/2205.11357 - Benjamins, C., Eimer, T., Schubert, F., Mohan, A., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2022)Contextualize Me - The Case for Context in Reinforcement Learning, ArXiv Preprint.
@article{BenEim2022a,
author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Mohan, Aditya and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
journal = {ArXiv Preprint},
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month = {02},
title = {Contextualize Me - The Case for Context in Reinforcement Learning},
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%A Benjamins, Carolin
%A Eimer, Theresa
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%A Hutter, Frank
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%T Contextualize Me - The Case for Context in Reinforcement Learning
%U https://arxiv.org/abs/2202.04500 - Norrenbrock, T., Marco, R., and Rosenhahn, B. (2022)Take 5: Interpretable Image Classification with a Handful of Features. In Progress and Challenges in Building Trustworthy Embodied AI @NeurIPS.
@inproceedings{NorRud2022a,
author = {Norrenbrock, Thomas and Marco, Rudolph and Rosenhahn, Bodo},
booktitle = {Progress and Challenges in Building Trustworthy Embodied AI @NeurIPS},
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title = {Take 5: Interpretable Image Classification with a Handful of Features},
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%A Norrenbrock, Thomas
%A Marco, Rudolph
%A Rosenhahn, Bodo
%B Progress and Challenges in Building Trustworthy Embodied AI @NeurIPS
%D 2022
%T Take 5: Interpretable Image Classification with a Handful of Features - Xu, L., Perez-Liebana, D., and Dockhorn, A. (2022)Towards Applicable State Abstractions: a Preview in Strategy. In The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) - RL as a Model of Agency, pp. 1–7.
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author = {Xu, Linjie and Perez-Liebana, Diego and Dockhorn, Alexander},
booktitle = {The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) - RL as a Model of Agency},
keywords = {Towards},
pages = {1-7},
title = {Towards Applicable State Abstractions: a Preview in Strategy},
year = 2022
}%0 Conference Paper
%1 XuPer2022
%A Xu, Linjie
%A Perez-Liebana, Diego
%A Dockhorn, Alexander
%B The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) - RL as a Model of Agency
%D 2022
%P 1-7
%T Towards Applicable State Abstractions: a Preview in Strategy
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author = {Adhisantoso, Yeremia Gunawan},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {Recommendation},
month = 10,
title = {Recommendation on MPEG-G Part 6 Record Structure m61340},
year = 2022
}%0 Journal Article
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%A Adhisantoso, Yeremia Gunawan
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%J ISO/IEC JTC 1/SC 29/WG 8
%T Recommendation on MPEG-G Part 6 Record Structure m61340 - Gebauer, C., Dengler, N., and Bennewitz, M. (2022)Sensor-Based Navigation Using Hierarchical Reinforcement Learning. In International Conference on Intelligent Autonomous Systems (IAS).
@inproceedings{GebDen2022,
author = {Gebauer, Christopher and Dengler, Nils and Bennewitz, Maren},
booktitle = {International Conference on Intelligent Autonomous Systems (IAS)},
keywords = {Sensor-Based},
title = {Sensor-Based Navigation Using Hierarchical Reinforcement Learning},
year = 2022
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%1 GebDen2022
%A Gebauer, Christopher
%A Dengler, Nils
%A Bennewitz, Maren
%B International Conference on Intelligent Autonomous Systems (IAS)
%D 2022
%T Sensor-Based Navigation Using Hierarchical Reinforcement Learning - Spliethöver, M., Keiff, M., and Wachsmuth, H. (2022)No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media. In Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Association for Computational Linguistics.
@inproceedings{Spliethöver_Keiff_Wachsmuth_2022,
author = {Spliethöver, Maximilian and Keiff, Maximilian and Wachsmuth, Henning},
booktitle = {Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)},
keywords = {leibnizailab},
publisher = {Association for Computational Linguistics},
title = {No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media},
year = 2022
}%0 Conference Paper
%1 Spliethöver_Keiff_Wachsmuth_2022
%A Spliethöver, Maximilian
%A Keiff, Maximilian
%A Wachsmuth, Henning
%B Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)
%D 2022
%I Association for Computational Linguistics
%T No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media - Mallik, N., Hvarfner, C., Stoll, D., Janowski, M., Bergman, E., Lindauer, M., Nardi, L., and Hutter, F. (2022)PriorBand: HyperBand + Human Expert Knowledge. In Workshop on Meta-Learning (MetaLearn 2022).
@inproceedings{mallik2022priorband,
author = {Mallik, Neeratyoy and Hvarfner, Carl and Stoll, Danny and Janowski, Maciej and Bergman, Eddie and Lindauer, Marius and Nardi, Luigi and Hutter, Frank},
booktitle = {Workshop on Meta-Learning (MetaLearn 2022)},
keywords = {leibnizailab},
title = {PriorBand: HyperBand + Human Expert Knowledge},
year = 2022
}%0 Conference Paper
%1 mallik2022priorband
%A Mallik, Neeratyoy
%A Hvarfner, Carl
%A Stoll, Danny
%A Janowski, Maciej
%A Bergman, Eddie
%A Lindauer, Marius
%A Nardi, Luigi
%A Hutter, Frank
%B Workshop on Meta-Learning (MetaLearn 2022)
%D 2022
%T PriorBand: HyperBand + Human Expert Knowledge
%U https://openreview.net/forum?id=ds21dwfBBH - Sengupta, M., Alshomary, M., and Wachsmuth, H. (2022)Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning. In Proceedings of the 2022 Workshop on Figurative Language Processing.
@inproceedings{Sengupta_Alshomary_Wachsmuth_2022,
author = {Sengupta, Meghdut and Alshomary, Milad and Wachsmuth, Henning},
booktitle = {Proceedings of the 2022 Workshop on Figurative Language Processing},
keywords = {leibnizailab},
title = {Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning},
year = 2022
}%0 Conference Paper
%1 Sengupta_Alshomary_Wachsmuth_2022
%A Sengupta, Meghdut
%A Alshomary, Milad
%A Wachsmuth, Henning
%B Proceedings of the 2022 Workshop on Figurative Language Processing
%D 2022
%T Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning - Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., and Lindauer, M. (2022)MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information.Selecting a well-performing algorithm for a given task or dataset can be time-consuming andtedious, but is crucial for the successful day-to-day business of developing new AI & MLapplications. Algorithm Selection (AS) mitigates this through a meta-model leveragingmeta-information about previous tasks. However, most of the available AS methods areerror-prone because they characterize a task by either cheap-to-compute properties of thedataset or evaluations of cheap proxy algorithms, called landmarks. In this work, we extendthe classical AS data setup to include multi-fidelity information and empirically demonstratehow meta-learning on algorithms{\textquoteright} learning behaviour allows us to exploit cheap test-timeevidence effectively and combat myopia significantly. We further postulate a budget-regrettrade-off w.r.t. the selection process. Our new selector MASIF is able to jointly interpretonline evidence on a task in form of varying-length learning curves without any parametricassumption by leveraging a transformer-based encoder. This opens up new possibilities forguided rapid prototyping in data science on cheaply observed partial learning curves.
@techreport{91a29f974fce4959967ea6759e1075f4,
abstract = {Selecting a well-performing algorithm for a given task or dataset can be time-consuming andtedious, but is crucial for the successful day-to-day business of developing new AI & MLapplications. Algorithm Selection (AS) mitigates this through a meta-model leveragingmeta-information about previous tasks. However, most of the available AS methods areerror-prone because they characterize a task by either cheap-to-compute properties of thedataset or evaluations of cheap proxy algorithms, called landmarks. In this work, we extendthe classical AS data setup to include multi-fidelity information and empirically demonstratehow meta-learning on algorithms{\textquoteright} learning behaviour allows us to exploit cheap test-timeevidence effectively and combat myopia significantly. We further postulate a budget-regrettrade-off w.r.t. the selection process. Our new selector MASIF is able to jointly interpretonline evidence on a task in form of varying-length learning curves without any parametricassumption by leveraging a transformer-based encoder. This opens up new possibilities forguided rapid prototyping in data science on cheaply observed partial learning curves.},
author = {Ruhkopf, Tim and Mohan, Aditya and Deng, Difan and Tornede, Alexander and Hutter, Frank and Lindauer, Marius},
keywords = {leibnizailab},
month = 12,
title = {MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information},
type = {WorkingPaper},
year = 2022
}%0 Report
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%A Ruhkopf, Tim
%A Mohan, Aditya
%A Deng, Difan
%A Tornede, Alexander
%A Hutter, Frank
%A Lindauer, Marius
%D 2022
%T MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information
%X Selecting a well-performing algorithm for a given task or dataset can be time-consuming andtedious, but is crucial for the successful day-to-day business of developing new AI & MLapplications. Algorithm Selection (AS) mitigates this through a meta-model leveragingmeta-information about previous tasks. However, most of the available AS methods areerror-prone because they characterize a task by either cheap-to-compute properties of thedataset or evaluations of cheap proxy algorithms, called landmarks. In this work, we extendthe classical AS data setup to include multi-fidelity information and empirically demonstratehow meta-learning on algorithms{\textquoteright} learning behaviour allows us to exploit cheap test-timeevidence effectively and combat myopia significantly. We further postulate a budget-regrettrade-off w.r.t. the selection process. Our new selector MASIF is able to jointly interpretonline evidence on a task in form of varying-length learning curves without any parametricassumption by leveraging a transformer-based encoder. This opens up new possibilities forguided rapid prototyping in data science on cheaply observed partial learning curves. - Wittig, A., Miranda, F., Hölzer, M., Altenburg, T., Bartoszewicz, J. M., Beyvers, S., Dieckmann, M. A., Genske, U., Giese, S. H., Nowicka, M., Richard, H., Schiebenhoefer, H., Schmachtenberg, A.-J., Sieben, P., Tang, M., Tembrockhaus, J., Renard, B. Y., and Fuchs, S. (2022){CovRadar}: continuously tracking and filtering {SARS}-{CoV}-2 mutations for genomic surveillance, Bioinformatics (Kelso, J., Ed.), Oxford University Press ({OUP}) 38, 4223–4225.
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author = {Wittig, Alice and Miranda, F{{á}}bio and Hölzer, Martin and Altenburg, Tom and Bartoszewicz, Jakub M and Beyvers, Sebastian and Dieckmann, Marius A and Genske, Ulrich and Giese, Sven H and Nowicka, Melania and Richard, Hugues and Schiebenhoefer, Henning and Schmachtenberg, Anna-Juliane and Sieben, Paul and Tang, Ming and Tembrockhaus, Julius and Renard, Bernhard Y and Fuchs, Stephan},
editor = {Kelso, Janet},
journal = {Bioinformatics},
keywords = {l3s},
month = {07},
number = 17,
pages = {4223--4225},
publisher = {Oxford University Press ({OUP})},
title = {{CovRadar}: continuously tracking and filtering {SARS}-{CoV}-2 mutations for genomic surveillance},
volume = 38,
year = 2022
}%0 Journal Article
%1 Wittig_2022
%A Wittig, Alice
%A Miranda, F{{á}}bio
%A Hölzer, Martin
%A Altenburg, Tom
%A Bartoszewicz, Jakub M
%A Beyvers, Sebastian
%A Dieckmann, Marius A
%A Genske, Ulrich
%A Giese, Sven H
%A Nowicka, Melania
%A Richard, Hugues
%A Schiebenhoefer, Henning
%A Schmachtenberg, Anna-Juliane
%A Sieben, Paul
%A Tang, Ming
%A Tembrockhaus, Julius
%A Renard, Bernhard Y
%A Fuchs, Stephan
%D 2022
%E Kelso, Janet
%I Oxford University Press ({OUP})
%J Bioinformatics
%N 17
%P 4223--4225
%R 10.1093/bioinformatics/btac411
%T {CovRadar}: continuously tracking and filtering {SARS}-{CoV}-2 mutations for genomic surveillance
%U https://doi.org/10.1093%2Fbioinformatics%2Fbtac411
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author = {Bode, L. and Schamer, S. and Bohnke, J. and Bott, O. J. and Marschollek, M. and Jack, T. and Wulff, A. and Group, Elise Study},
journal = {Appl Clin Inform},
keywords = {l3s},
number = 5,
pages = {1002-1014},
title = {Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction},
type = {Journal Article},
volume = 13,
year = 2022
}%0 Journal Article
%1 RN1
%A Bode, L.
%A Schamer, S.
%A Bohnke, J.
%A Bott, O. J.
%A Marschollek, M.
%A Jack, T.
%A Wulff, A.
%A Group, Elise Study
%D 2022
%J Appl Clin Inform
%N 5
%P 1002-1014
%R 10.1055/a-1950-9637
%T Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction
%U https://www.ncbi.nlm.nih.gov/pubmed/36162433
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@article{KaiEhm2022a,
author = {Kaiser, Timo and Ehmann, Lukas and Reinders, Christoph and Rosenhahn, Bodo},
journal = {arXiv},
keywords = {Knowledge},
month = 11,
title = {Blind Knowledge Distillation for Robust Image Classification},
year = 2022
}%0 Journal Article
%1 KaiEhm2022a
%A Kaiser, Timo
%A Ehmann, Lukas
%A Reinders, Christoph
%A Rosenhahn, Bodo
%D 2022
%J arXiv
%R 10.48550/ARXIV.2211.11355
%T Blind Knowledge Distillation for Robust Image Classification
%U https://arxiv.org/abs/2211.11355 - Chen, M.-H., Mudgal, G., Chen, W.-F., and Wachsmuth, H. (2022)Investigating the argumentation structures of EFL learners from diverse language backgrounds. In EUROCALL.
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author = {Chen, Mei-Hua and Mudgal, Garima and Chen, Wei-Fan and Wachsmuth, Henning},
booktitle = {EUROCALL},
keywords = {leibnizailab},
title = {Investigating the argumentation structures of EFL learners from diverse language backgrounds},
year = 2022
}%0 Conference Paper
%1 Chen_Mudgal_Chen_Wachsmuth_2022
%A Chen, Mei-Hua
%A Mudgal, Garima
%A Chen, Wei-Fan
%A Wachsmuth, Henning
%B EUROCALL
%D 2022
%T Investigating the argumentation structures of EFL learners from diverse language backgrounds - Geisler, S., Vidal, M.-E., Cappiello, C., Loscio, B. F., Gal, A., Jarke, M., Lenzerini, M., Missier, P., Otto, B., Paja, E., Pernici, B., and Rehof, J. (2022)Knowledge-Driven Data Ecosystems Toward Data Transparency, Journal of Data and Information Quality, Association for Computing Machinery ({ACM}) 14, 1–12.
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author = {Geisler, Sandra and Vidal, Maria-Esther and Cappiello, Cinzia and Loscio, Bernadette Farias and Gal, Avigdor and Jarke, Matthias and Lenzerini, Maurizio and Missier, Paolo and Otto, Boris and Paja, Elda and Pernici, Barbara and Rehof, Jakob},
journal = {Journal of Data and Information Quality},
keywords = {semanticdataintegration},
month = {03},
number = 1,
pages = {1--12},
publisher = {Association for Computing Machinery ({ACM})},
title = {Knowledge-Driven Data Ecosystems Toward Data Transparency},
volume = 14,
year = 2022
}%0 Journal Article
%1 Geisler_2022
%A Geisler, Sandra
%A Vidal, Maria-Esther
%A Cappiello, Cinzia
%A Loscio, Bernadette Farias
%A Gal, Avigdor
%A Jarke, Matthias
%A Lenzerini, Maurizio
%A Missier, Paolo
%A Otto, Boris
%A Paja, Elda
%A Pernici, Barbara
%A Rehof, Jakob
%D 2022
%I Association for Computing Machinery ({ACM})
%J Journal of Data and Information Quality
%N 1
%P 1--12
%R 10.1145/3467022
%T Knowledge-Driven Data Ecosystems Toward Data Transparency
%U https://doi.org/10.1145%2F3467022
%V 14 - Moosbauer, J., Casalicchio, G., Lindauer, M., and Bischl, B. (2022)Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution, Arxiv Preprint.Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves. This makes it difficult to understand the decision process which lead to the selected configuration, reduces trust in HPO, and thus hinders its broad adoption. Here, we study the combination of HPO with interpretable machine learning (IML) methods such as partial dependence plots. However, if such methods are naively applied to the experimental data of the HPO process in a post-hoc manner, the underlying sampling bias of the optimizer can distort interpretations. We propose a modified HPO method which efficiently balances the search for the global optimum w.r.t. predictive performance and the reliable estimation of IML explanations of an underlying black-box function by coupling Bayesian optimization and Bayesian Algorithm Execution. On benchmark cases of both synthetic objectives and HPO of a neural network, we demonstrate that our method returns more reliable explanations of the underlying black-box without a loss of optimization performance.
@article{738b79fe27834cd6ba4b98716c851398,
abstract = {Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves. This makes it difficult to understand the decision process which lead to the selected configuration, reduces trust in HPO, and thus hinders its broad adoption. Here, we study the combination of HPO with interpretable machine learning (IML) methods such as partial dependence plots. However, if such methods are naively applied to the experimental data of the HPO process in a post-hoc manner, the underlying sampling bias of the optimizer can distort interpretations. We propose a modified HPO method which efficiently balances the search for the global optimum w.r.t. predictive performance and the reliable estimation of IML explanations of an underlying black-box function by coupling Bayesian optimization and Bayesian Algorithm Execution. On benchmark cases of both synthetic objectives and HPO of a neural network, we demonstrate that our method returns more reliable explanations of the underlying black-box without a loss of optimization performance.},
author = {Moosbauer, Julia and Casalicchio, Giuseppe and Lindauer, Marius and Bischl, Bernd},
journal = {Arxiv Preprint},
keywords = {leibnizailab},
month = {06},
title = {Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution},
type = {WorkingPaper},
year = 2022
}%0 Journal Article
%1 738b79fe27834cd6ba4b98716c851398
%A Moosbauer, Julia
%A Casalicchio, Giuseppe
%A Lindauer, Marius
%A Bischl, Bernd
%D 2022
%J Arxiv Preprint
%R 10.48550/arXiv.2206.05447
%T Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution
%X Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves. This makes it difficult to understand the decision process which lead to the selected configuration, reduces trust in HPO, and thus hinders its broad adoption. Here, we study the combination of HPO with interpretable machine learning (IML) methods such as partial dependence plots. However, if such methods are naively applied to the experimental data of the HPO process in a post-hoc manner, the underlying sampling bias of the optimizer can distort interpretations. We propose a modified HPO method which efficiently balances the search for the global optimum w.r.t. predictive performance and the reliable estimation of IML explanations of an underlying black-box function by coupling Bayesian optimization and Bayesian Algorithm Execution. On benchmark cases of both synthetic objectives and HPO of a neural network, we demonstrate that our method returns more reliable explanations of the underlying black-box without a loss of optimization performance. - Nayak, T., Sharma, S., Butala, Y., Dasgupta, K., Goyal, P., and Ganguly, N. (2022)A Generative Approach for Financial Causality Extraction. In Companion Proceedings of the Web Conference 2022, {ACM}.
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author = {Nayak, Tapas and Sharma, Soumya and Butala, Yash and Dasgupta, Koustuv and Goyal, Pawan and Ganguly, Niloy},
booktitle = {Companion Proceedings of the Web Conference 2022},
keywords = {"sys:relevantfor:l3s"},
month = {04},
publisher = {{ACM}},
title = {A Generative Approach for Financial Causality Extraction},
year = 2022
}%0 Conference Paper
%1 Nayak_2022
%A Nayak, Tapas
%A Sharma, Soumya
%A Butala, Yash
%A Dasgupta, Koustuv
%A Goyal, Pawan
%A Ganguly, Niloy
%B Companion Proceedings of the Web Conference 2022
%D 2022
%I {ACM}
%R 10.1145/3487553.3524633
%T A Generative Approach for Financial Causality Extraction
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author = {Mast, M. and Marschollek, M. and Jack, T. and Wulff, A. and Elise Study, Group},
journal = {Stud Health Technol Inform},
keywords = {l3s},
pages = {228-231},
title = {Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data},
type = {Journal Article},
volume = 289,
year = 2022
}%0 Journal Article
%1 RN11
%A Mast, M.
%A Marschollek, M.
%A Jack, T.
%A Wulff, A.
%A Elise Study, Group
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%J Stud Health Technol Inform
%P 228-231
%R 10.3233/SHTI210901
%T Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data
%U https://www.ncbi.nlm.nih.gov/pubmed/35062134
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journal = {Arxiv Preprint},
keywords = {POLTER},
month = {05},
title = {POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning},
year = 2022
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%1 SchBen2022
%A Schubert, Frederik
%A Benjamins, Carolin
%A D{ö}hler, Sebastian
%A Rosenhahn, Bodo
%A Lindauer, Marius
%D 2022
%J Arxiv Preprint
%T POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
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author = {Chen, Wei-Fan and Chen, Mei-Hua and Mudgal, Garima and Wachsmuth, Henning},
booktitle = {Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)},
keywords = {leibnizailab},
pages = {51–61},
title = {Analyzing Culture-Specific Argument Structures in Learner Essays},
year = 2022
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%1 Chen_Chen_Mudgal_Wachsmuth_2022
%A Chen, Wei-Fan
%A Chen, Mei-Hua
%A Mudgal, Garima
%A Wachsmuth, Henning
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%D 2022
%P 51–61
%T Analyzing Culture-Specific Argument Structures in Learner Essays - Deng, D., Karl, F., Hutter, F., Bischl, B., and Lindauer, M. (2022)Efficient Automated Deep Learning for Time Series Forecasting. In Proceedings of the European Conference on Machine Learning (ECML), arXiv.
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author = {Deng, Difan and Karl, Florian and Hutter, Frank and Bischl, Bernd and Lindauer, Marius},
booktitle = {Proceedings of the European Conference on Machine Learning (ECML)},
keywords = {leibnizailab},
publisher = {arXiv},
title = {Efficient Automated Deep Learning for Time Series Forecasting},
year = 2022
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%1 https://doi.org/10.48550/arxiv.2205.05511
%A Deng, Difan
%A Karl, Florian
%A Hutter, Frank
%A Bischl, Bernd
%A Lindauer, Marius
%B Proceedings of the European Conference on Machine Learning (ECML)
%D 2022
%I arXiv
%R 10.48550/ARXIV.2205.05511
%T Efficient Automated Deep Learning for Time Series Forecasting
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author = {Mohan, Aditya and Ruhkopf, Tim and Lindauer, Marius},
booktitle = {ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)},
keywords = {leibnizailab},
publisher = {arXiv},
title = {Towards Meta-learned Algorithm Selection using Implicit Fidelity Information},
year = 2022
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%A Mohan, Aditya
%A Ruhkopf, Tim
%A Lindauer, Marius
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%D 2022
%I arXiv
%R 10.48550/ARXIV.2206.03130
%T Towards Meta-learned Algorithm Selection using Implicit Fidelity Information
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@inproceedings{ReiSch2022,
author = {Reinders, Christoph and Schubert, Frederik and Rosenhahn, Bodo},
booktitle = {Arxiv Preprint},
keywords = {Feature},
month = {03},
title = {ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing},
year = 2022
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%A Reinders, Christoph
%A Schubert, Frederik
%A Rosenhahn, Bodo
%B Arxiv Preprint
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%T ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing - Benjamins, C., Raponi, E., Jankovic, A., van der Blom, K., Santoni, M. L., Lindauer, M., and Doerr, C. (2022)PI is back! Switching Acquisition Functions in Bayesian Optimization. In 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, arXiv.
@inproceedings{https://doi.org/10.48550/arxiv.2211.01455,
author = {Benjamins, Carolin and Raponi, Elena and Jankovic, Anja and van der Blom, Koen and Santoni, Maria Laura and Lindauer, Marius and Doerr, Carola},
booktitle = {2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems},
keywords = {leibnizailab},
publisher = {arXiv},
title = {PI is back! Switching Acquisition Functions in Bayesian Optimization},
year = 2022
}%0 Conference Paper
%1 https://doi.org/10.48550/arxiv.2211.01455
%A Benjamins, Carolin
%A Raponi, Elena
%A Jankovic, Anja
%A van der Blom, Koen
%A Santoni, Maria Laura
%A Lindauer, Marius
%A Doerr, Carola
%B 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
%D 2022
%I arXiv
%R 10.48550/ARXIV.2211.01455
%T PI is back! Switching Acquisition Functions in Bayesian Optimization
%U https://arxiv.org/abs/2211.01455 - Adhisantoso, Y. G., Xuan, Q. L., Kellerman, C., Munderloh, M., and Ostermann, J. (2022)Introduction to deep degradation metric in smart production ecosystems. In 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME.
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author = {Adhisantoso, Yeremia Gunawan and Xuan, Quy Le and Kellerman, Christoph and Munderloh, Marco and Ostermann, J{ö}rn},
booktitle = {16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME},
keywords = {to},
title = {Introduction to deep degradation metric in smart production ecosystems},
year = 2022
}%0 Conference Paper
%1 AdhLeX2022
%A Adhisantoso, Yeremia Gunawan
%A Xuan, Quy Le
%A Kellerman, Christoph
%A Munderloh, Marco
%A Ostermann, J{ö}rn
%B 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME
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author = {Dockhorn, Alexander and Kruse, Rudolf},
journal = {Advances in Intelligent Systems Research and Innovation},
keywords = {Exploration},
pages = {1--19},
title = {Balancing Exploration and Exploitation in Forward Model Learning},
year = 2022
}%0 Journal Article
%1 DocKru2022
%A Dockhorn, Alexander
%A Kruse, Rudolf
%D 2022
%J Advances in Intelligent Systems Research and Innovation
%P 1--19
%R 10.1007/978-3-030-78124-8_1
%T Balancing Exploration and Exploitation in Forward Model Learning
%U https://doi.org/10.1007/978-3-030-78124-8_1
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author = {Alshomary, Milad and El Baff, Roxanne and Gurcke, Timon and Wachsmuth, Henning},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
keywords = {leibnizailab},
pages = {8782–8797},
title = {The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments},
year = 2022
}%0 Conference Paper
%1 alshomary2022moral
%A Alshomary, Milad
%A El Baff, Roxanne
%A Gurcke, Timon
%A Wachsmuth, Henning
%B Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics
%D 2022
%P 8782–8797
%T The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments - Adhisantoso, Y. G. (2022)Cross-check of EPFL’s response to core experiment 3 on indexing DNA sequences in the compressed domain m61082, ISO/IEC JTC 1/SC 29/WG 8.
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author = {Adhisantoso, Yeremia Gunawan},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {EPFL's},
month = 10,
title = {Cross-check of EPFL's response to core experiment 3 on indexing DNA sequences in the compressed domain m61082},
year = 2022
}%0 Journal Article
%1 Adh2022b
%A Adhisantoso, Yeremia Gunawan
%D 2022
%J ISO/IEC JTC 1/SC 29/WG 8
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booktitle = {Workshop on Meta-Learning (MetaLearn 2022)},
keywords = {leibnizailab},
title = {Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis},
year = 2022
}%0 Conference Paper
%1 benjamins2022towards
%A Benjamins, Carolin
%A Jankovic, Anja
%A Raponi, Elena
%A van der Blom, Koen
%A Lindauer, Marius
%A Doerr, Carola
%B Workshop on Meta-Learning (MetaLearn 2022)
%D 2022
%T Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
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booktitle = {ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)},
keywords = {leibnizailab},
publisher = {arXiv},
title = {DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning},
year = 2022
}%0 Conference Paper
%1 https://doi.org/10.48550/arxiv.2206.03493
%A Sass, René
%A Bergman, Eddie
%A Biedenkapp, André
%A Hutter, Frank
%A Lindauer, Marius
%B ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)
%D 2022
%I arXiv
%R 10.48550/ARXIV.2206.03493
%T DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
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author = {Chang, Yi and Ren, Zhao and Nguyen, Thanh Tam and Nejdl, Wolfgang and Schuller, Bj{{ö}}rn W.},
booktitle = {Interspeech 2022, 23rd Annual Conference of the International Speech Communication Association, Incheon, Korea, 18-22 September 2022},
keywords = {leibnizailab},
pages = {4003--4007},
publisher = {{ISCA}},
title = {Example-based explanations with adversarial attacks for respiratory sound analysis},
year = 2022
}%0 Conference Paper
%1 DBLP:conf/interspeech/ChangRNNS22
%A Chang, Yi
%A Ren, Zhao
%A Nguyen, Thanh Tam
%A Nejdl, Wolfgang
%A Schuller, Bj{{ö}}rn W.
%B Interspeech 2022, 23rd Annual Conference of the International Speech Communication Association, Incheon, Korea, 18-22 September 2022
%D 2022
%I {ISCA}
%P 4003--4007
%R 10.21437/Interspeech.2022-11355
%T Example-based explanations with adversarial attacks for respiratory sound analysis
%U https://doi.org/10.21437/Interspeech.2022-11355 - Hinrichs, R., Gerkens, K., and Ostermann, J. (2022)Convolutional Neural Networks for the Classification of Guitar Effects and Extraction of the Parameter Settings of Single and Multi Guitar Effects from Instrument Mixes, EURASIP Journal on Audio, Speech, and Music Processing.
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author = {Hinrichs, Reemt and Gerkens, Kevin and Ostermann, J{ö}rn},
journal = {EURASIP Journal on Audio, Speech, and Music Processing},
keywords = {Neural},
title = {Convolutional Neural Networks for the Classification of Guitar Effects and Extraction of the Parameter Settings of Single and Multi Guitar Effects from Instrument Mixes},
year = 2022
}%0 Journal Article
%1 HinGer2022a
%A Hinrichs, Reemt
%A Gerkens, Kevin
%A Ostermann, J{ö}rn
%D 2022
%J EURASIP Journal on Audio, Speech, and Music Processing
%T Convolutional Neural Networks for the Classification of Guitar Effects and Extraction of the Parameter Settings of Single and Multi Guitar Effects from Instrument Mixes - Pestel-Schiller, U., Yang, Y., and Ostermann, J. (2022)Semantic Segmentation of Natural and Man-Made Fruits Using a Spatial-Spectral Two-Channel-CNN for Sparse Data. In 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS).
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author = {Pestel-Schiller, Ulrike and Yang, Ye and Ostermann, J{ö}rn},
booktitle = {12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
keywords = {Segmantic},
month = {09},
title = {Semantic Segmentation of Natural and Man-Made Fruits Using a Spatial-Spectral Two-Channel-CNN for Sparse Data},
year = 2022
}%0 Conference Paper
%1 PesYan2022
%A Pestel-Schiller, Ulrike
%A Yang, Ye
%A Ostermann, J{ö}rn
%B 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
%D 2022
%T Semantic Segmentation of Natural and Man-Made Fruits Using a Spatial-Spectral Two-Channel-CNN for Sparse Data - Rosenhahn, B. (2022)Mixed Integer Linear Programming for Optimizing a Hopfield Network. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 1–17.
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keywords = {Mixed},
month = {09},
pages = {1-17},
title = {Mixed Integer Linear Programming for Optimizing a Hopfield Network},
year = 2022
}%0 Conference Paper
%1 Bod2022a
%A Rosenhahn, Bodo
%B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)
%D 2022
%P 1-17
%T Mixed Integer Linear Programming for Optimizing a Hopfield Network - Dong, T. N., Schrader, J., M{ü}cke, S., and Khosla, M. (2022)A Message Passing framework with Multiple data integration for miRNA-Disease association prediction, Scientific Reports 16259.
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author = {Dong, Thi Ngan and Schrader, Johanna and M{ü}cke, Stefanie and Khosla, Megha},
journal = {Scientific Reports},
keywords = {leibnizailab},
pages = 16259,
title = {A Message Passing framework with Multiple data integration for miRNA-Disease association prediction},
year = 2022
}%0 Journal Article
%1 dong2022message
%A Dong, Thi Ngan
%A Schrader, Johanna
%A M{ü}cke, Stefanie
%A Khosla, Megha
%D 2022
%J Scientific Reports
%P 16259
%T A Message Passing framework with Multiple data integration for miRNA-Disease association prediction - Parker-Holder, J., Rajan, R., Song, X., Biedenkapp, A., Miao, Y., Eimer, T., Zhang, B., Nguyen, V., Calandra, R., Faust, A., Hutter, F., and Lindauer, M. (2022)Automated Reinforcement Learning (AutoRL): A Survey and Open Problems, Journal of Artificial Intelligence Research 74 (2022) 517–568.
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journal = {Journal of Artificial Intelligence Research 74 (2022)},
keywords = {AutoML},
pages = {517-568},
title = {Automated Reinforcement Learning (AutoRL): A Survey and Open Problems},
year = 2022
}%0 Journal Article
%1 2201.03916
%A Parker-Holder, Jack
%A Rajan, Raghu
%A Song, Xingyou
%A Biedenkapp, André
%A Miao, Yingjie
%A Eimer, Theresa
%A Zhang, Baohe
%A Nguyen, Vu
%A Calandra, Roberto
%A Faust, Aleksandra
%A Hutter, Frank
%A Lindauer, Marius
%D 2022
%J Journal of Artificial Intelligence Research 74 (2022)
%P 517-568
%R 10.1613/jair.1.13596
%T Automated Reinforcement Learning (AutoRL): A Survey and Open Problems - Dong, T. N., Mucke, S., and Khosla, M. (2022)MuCoMiD: A Multitask graph Convolutional Learning Framework for miRNA-Disease Association Prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE.
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keywords = {leibnizailab},
publisher = {IEEE},
title = {MuCoMiD: A Multitask graph Convolutional Learning Framework for miRNA-Disease Association Prediction},
year = 2022
}%0 Journal Article
%1 dong2022mucomid
%A Dong, Thi Ngan
%A Mucke, Stefanie
%A Khosla, Megha
%D 2022
%I IEEE
%J IEEE/ACM Transactions on Computational Biology and Bioinformatics
%T MuCoMiD: A Multitask graph Convolutional Learning Framework for miRNA-Disease Association Prediction - Dockhorn, A., Kirst, M., Mostaghim, S., Wieczorek, M., and Zille, H. (2022)Evolutionary Algorithm for Parameter Optimization of Context Steering Agents, IEEE Transactions on Games 1–12.
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keywords = {Algorithm},
pages = {1-12},
title = {Evolutionary Algorithm for Parameter Optimization of Context Steering Agents},
year = 2022
}%0 Journal Article
%1 DocKir2022
%A Dockhorn, Alexander
%A Kirst, Martin
%A Mostaghim, Sanaz
%A Wieczorek, Martin
%A Zille, Heiner
%D 2022
%J IEEE Transactions on Games
%P 1-12
%R 10.1109/TG.2022.3157247
%T Evolutionary Algorithm for Parameter Optimization of Context Steering Agents
%U https://ieeexplore.ieee.org/document/9729529 - Chang, Y., Jing, X., Ren, Z., and Schuller, B. W. (2022)CovNet: A transfer learning framework for automatic COVID-19 detection from crowd-sourced cough sounds, Frontiers in Digital Health (Hochheiser, H., Ed.) 3, 1–11.
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author = {Chang, Yi and Jing, Xin and Ren, Zhao and Schuller, Björn W.},
editor = {Hochheiser, Harry},
journal = {Frontiers in Digital Health},
keywords = {leibnizailab},
month = {01},
number = 799067,
pages = {1--11},
title = {CovNet: A transfer learning framework for automatic COVID-19 detection from crowd-sourced cough sounds},
volume = 3,
year = 2022
}%0 Journal Article
%1 chang2022covnet
%A Chang, Yi
%A Jing, Xin
%A Ren, Zhao
%A Schuller, Björn W.
%D 2022
%E Hochheiser, Harry
%J Frontiers in Digital Health
%N 799067
%P 1--11
%R https://doi.org/10.3389/fdgth.2021.799067
%T CovNet: A transfer learning framework for automatic COVID-19 detection from crowd-sourced cough sounds
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author = {Hu, Kaiqin and Liao, Wentong and Yang, Michael and Rosenhahn, Bodo},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
keywords = {Semantic-Spatial},
month = {06},
pages = {18166-18175},
title = {Text to Image Generation with Semantic-Spatial Aware GAN},
year = 2022
}%0 Conference Paper
%1 HuLia2022a
%A Hu, Kaiqin
%A Liao, Wentong
%A Yang, Michael
%A Rosenhahn, Bodo
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
%D 2022
%P 18166-18175
%R 10.1109/CVPR52688.2022.01765
%T Text to Image Generation with Semantic-Spatial Aware GAN
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author = {Rumberg, Lars and Gebauer, Christopher and Ehlert, Hanna and L{ü}dtke, Ulrike and Ostermann, J{ö}rn},
booktitle = {Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association},
keywords = {Improving},
month = {09},
title = {Improving Phonetic Transcriptions of Children’s Speech by Pronunciation Modelling with Constrained CTC-Decoding},
year = 2022
}%0 Conference Paper
%1 RumGeb2022b
%A Rumberg, Lars
%A Gebauer, Christopher
%A Ehlert, Hanna
%A L{ü}dtke, Ulrike
%A Ostermann, J{ö}rn
%B Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association
%D 2022
%T Improving Phonetic Transcriptions of Children’s Speech by Pronunciation Modelling with Constrained CTC-Decoding - Bondarenko, A., Fr{ö}be, M., Kiesel, J., Syed, S., Gurcke, T., Beloucif, M., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., and Hagen, M. (2022)Overview of Touch{é} 2022: Argument Retrieval, CEUR Workshop Proceedings, CEUR WS 3180, 2867–2903.This paper is a report on the third year of the Touch{é} lab on argument retrieval hosted at CLEF 2022. With the goal of supporting and promoting the research and development of new technologies for argument mining and argument analysis, we have organized three shared tasks: (a) argument retrieval for controversial topics, where the task is to find sentences that reflect the gist of arguments from online debates, (b) argument retrieval for comparative issues, where the task is to find argumentative passages from web documents that help in making a comparative decision, and (c) image retrieval for arguments, where the task is to find images that show support for or opposition to a particular stance.
@article{c1861a701a4f42559399a794a05b1b29,
abstract = {This paper is a report on the third year of the Touch{é} lab on argument retrieval hosted at CLEF 2022. With the goal of supporting and promoting the research and development of new technologies for argument mining and argument analysis, we have organized three shared tasks: (a) argument retrieval for controversial topics, where the task is to find sentences that reflect the gist of arguments from online debates, (b) argument retrieval for comparative issues, where the task is to find argumentative passages from web documents that help in making a comparative decision, and (c) image retrieval for arguments, where the task is to find images that show support for or opposition to a particular stance.},
author = {Bondarenko, Alexander and Fr{ö}be, Maik and Kiesel, Johannes and Syed, Shahbaz and Gurcke, Timon and Beloucif, Meriem and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and Potthast, Martin and Hagen, Matthias},
journal = {CEUR Workshop Proceedings},
keywords = {leibnizailab},
note = {Funding Information: This work was partially supported by the Deutsche Forschungsgemeinschaft (DFG) through the projects “ACQuA 2.0” (Answering Comparative Questions with Arguments; project number 376430233) and “OASiS” (Objective Argument Summarization in Search; project number 455913891) as part of the priority program “RATIO: Robust Argumentation Machines” (SPP 1999), and the German Ministry for Science and Education (BMBF) through the project “SharKI” (Shared Tasks as an Innovative Approach to Implement AI and Big Data-based Applications within Universities; grant FKZ 16DHB4021). We are also grateful to Jan Heinrich Reimer for developing the TARGER Python library and Erik Reuter for expanding a document collection for Task 2 with docT5query.; 2022 Conference and Labs of the Evaluation Forum, CLEF 2022 ; Conference date: 05-09-2022 Through 08-09-2022},
pages = {2867--2903},
publisher = {CEUR WS},
title = {Overview of Touch{é} 2022: Argument Retrieval},
volume = 3180,
year = 2022
}%0 Journal Article
%1 c1861a701a4f42559399a794a05b1b29
%A Bondarenko, Alexander
%A Fr{ö}be, Maik
%A Kiesel, Johannes
%A Syed, Shahbaz
%A Gurcke, Timon
%A Beloucif, Meriem
%A Panchenko, Alexander
%A Biemann, Chris
%A Stein, Benno
%A Wachsmuth, Henning
%A Potthast, Martin
%A Hagen, Matthias
%D 2022
%I CEUR WS
%J CEUR Workshop Proceedings
%P 2867--2903
%T Overview of Touch{é} 2022: Argument Retrieval
%V 3180
%X This paper is a report on the third year of the Touch{é} lab on argument retrieval hosted at CLEF 2022. With the goal of supporting and promoting the research and development of new technologies for argument mining and argument analysis, we have organized three shared tasks: (a) argument retrieval for controversial topics, where the task is to find sentences that reflect the gist of arguments from online debates, (b) argument retrieval for comparative issues, where the task is to find argumentative passages from web documents that help in making a comparative decision, and (c) image retrieval for arguments, where the task is to find images that show support for or opposition to a particular stance. - Adhisantoso, Y. G. (2022)Technical comments for Study on FDIS 23092-6 document m59160, ISO/IEC JTC 1/SC 29/WG 8.
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author = {Adhisantoso, Yeremia Gunawan},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {on},
month = {01},
title = {Technical comments for Study on FDIS 23092-6 document m59160},
year = 2022
}%0 Journal Article
%1 Adh2022a
%A Adhisantoso, Yeremia Gunawan
%D 2022
%J ISO/IEC JTC 1/SC 29/WG 8
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booktitle = {9th Asia-Pacific Workshops on Structural Health Monitoring 2022 (APWSHM 2022)},
keywords = {Localized},
month = 12,
title = {Localized Damage Detection in Wind Turbine Rotor Blades using Airborne Acoustic Emissions (accepted)},
year = 2022
}%0 Conference Paper
%1 LanHin2022
%A Lange, Alexander
%A Hinrichs, Reemt
%A Ostermann, J{ö}rn
%B 9th Asia-Pacific Workshops on Structural Health Monitoring 2022 (APWSHM 2022)
%D 2022
%T Localized Damage Detection in Wind Turbine Rotor Blades using Airborne Acoustic Emissions (accepted) - Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F. (2022)Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning, Journal of Machine Learning Research, Microtome Publishing 56.
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author = {Feurer, Matthias and Eggensperger, Katharina and Falkner, Stefan and Lindauer, Marius and Hutter, Frank},
journal = {Journal of Machine Learning Research},
keywords = {leibnizailab},
pages = 56,
publisher = {Microtome Publishing},
title = {Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning},
year = 2022
}%0 Journal Article
%1 https://doi.org/10.48550/arxiv.2007.04074
%A Feurer, Matthias
%A Eggensperger, Katharina
%A Falkner, Stefan
%A Lindauer, Marius
%A Hutter, Frank
%D 2022
%I Microtome Publishing
%J Journal of Machine Learning Research
%P 56
%R 10.48550/ARXIV.2007.04074
%T Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
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author = {Alshomary, Milad and Stahl, Maja},
booktitle = {Proceedings of the 9th Workshop on Argument Mining},
keywords = {leibnizailab},
pages = {111–114},
publisher = {International Conference on Computational Linguistics},
title = {Argument Novelty and Validity Assessment via Multitask and Transfer Learning},
year = 2022
}%0 Conference Paper
%1 Alshomary_Stahl_2022
%A Alshomary, Milad
%A Stahl, Maja
%B Proceedings of the 9th Workshop on Argument Mining
%D 2022
%I International Conference on Computational Linguistics
%P 111–114
%T Argument Novelty and Validity Assessment via Multitask and Transfer Learning - Xu, L., Hurtado-Grueso, J., Jeurissen, D., Liebana, D. P., and Dockhorn, A. (2022)Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing. In 2022 IEEE Conference on Games (CoG).
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booktitle = {2022 IEEE Conference on Games (CoG)},
keywords = {Monte},
title = {Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing},
year = 2022
}%0 Conference Paper
%1 XuHur2022
%A Xu, Linjie
%A Hurtado-Grueso, Jorge
%A Jeurissen, Dominic
%A Liebana, Diego Perez
%A Dockhorn, Alexander
%B 2022 IEEE Conference on Games (CoG)
%D 2022
%T Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing
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booktitle = {14th European Conference on Synthetic Aperture Radar},
keywords = {of},
month = {07},
title = {Impact of Spatial Resolution and Zoom on Interpreter-Based Evaluation of Compressed SAR Images},
year = 2022
}%0 Conference Paper
%1 PesOst2022
%A Pestel-Schiller, Ulrike
%A Ostermann, J{ö}rn
%B 14th European Conference on Synthetic Aperture Radar
%D 2022
%T Impact of Spatial Resolution and Zoom on Interpreter-Based Evaluation of Compressed SAR Images - Mukherjee, R., Vishnu, U., Peruri, H. C., Bhattacharya, S., Rudra, K., Goyal, P., and Ganguly, N. (2022)MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 755–763, Association for Computing Machinery, Virtual Event, AZ, USA.Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account of the unfolding event in near real-time to the consumers of such potentially unreliable information thus becomes an important task. In this work, we propose MTLTS, the first end-to-end solution for the task that jointly determines the credibility and summary-worthiness of tweets. Our credibility verifier is designed to recursively learn the structural properties of a Twitter conversation cascade, along with the stances of replies towards the source tweet. We then take a hierarchical multi-task learning approach, where the verifier is trained at a lower layer, and the summarizer is trained at a deeper layer where it utilizes the verifier predictions to determine the salience of a tweet. Different from existing disaster-specific summarizers, we model tweet summarization as a supervised task. Such an approach can automatically learn summary-worthy features, and can therefore generalize well across domains. When trained on the PHEME dataset [29], not only do we outperform the strongest baselines for the auxiliary task of verification/rumour detection, we also achieve 21 - 35% gains in the verified ratio of summary tweets, and 16 - 20% gains in ROUGE1-F1 scores over the existing state-of-the-art solutions for the primary task of trustworthy summarization.
@inproceedings{10.1145/3488560.3498536,
abstract = {Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account of the unfolding event in near real-time to the consumers of such potentially unreliable information thus becomes an important task. In this work, we propose MTLTS, the first end-to-end solution for the task that jointly determines the credibility and summary-worthiness of tweets. Our credibility verifier is designed to recursively learn the structural properties of a Twitter conversation cascade, along with the stances of replies towards the source tweet. We then take a hierarchical multi-task learning approach, where the verifier is trained at a lower layer, and the summarizer is trained at a deeper layer where it utilizes the verifier predictions to determine the salience of a tweet. Different from existing disaster-specific summarizers, we model tweet summarization as a supervised task. Such an approach can automatically learn summary-worthy features, and can therefore generalize well across domains. When trained on the PHEME dataset [29], not only do we outperform the strongest baselines for the auxiliary task of verification/rumour detection, we also achieve 21 - 35% gains in the verified ratio of summary tweets, and 16 - 20% gains in ROUGE1-F1 scores over the existing state-of-the-art solutions for the primary task of trustworthy summarization.},
address = {New York, NY, USA},
author = {Mukherjee, Rajdeep and Vishnu, Uppada and Peruri, Hari Chandana and Bhattacharya, Sourangshu and Rudra, Koustav and Goyal, Pawan and Ganguly, Niloy},
booktitle = {Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining},
keywords = {leibnizailab},
pages = {755–763},
publisher = {Association for Computing Machinery},
series = {WSDM '22},
title = {MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs},
year = 2022
}%0 Conference Paper
%1 10.1145/3488560.3498536
%A Mukherjee, Rajdeep
%A Vishnu, Uppada
%A Peruri, Hari Chandana
%A Bhattacharya, Sourangshu
%A Rudra, Koustav
%A Goyal, Pawan
%A Ganguly, Niloy
%B Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
%C New York, NY, USA
%D 2022
%I Association for Computing Machinery
%P 755–763
%R 10.1145/3488560.3498536
%T MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs
%U https://doi.org/10.1145/3488560.3498536
%X Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account of the unfolding event in near real-time to the consumers of such potentially unreliable information thus becomes an important task. In this work, we propose MTLTS, the first end-to-end solution for the task that jointly determines the credibility and summary-worthiness of tweets. Our credibility verifier is designed to recursively learn the structural properties of a Twitter conversation cascade, along with the stances of replies towards the source tweet. We then take a hierarchical multi-task learning approach, where the verifier is trained at a lower layer, and the summarizer is trained at a deeper layer where it utilizes the verifier predictions to determine the salience of a tweet. Different from existing disaster-specific summarizers, we model tweet summarization as a supervised task. Such an approach can automatically learn summary-worthy features, and can therefore generalize well across domains. When trained on the PHEME dataset [29], not only do we outperform the strongest baselines for the auxiliary task of verification/rumour detection, we also achieve 21 - 35% gains in the verified ratio of summary tweets, and 16 - 20% gains in ROUGE1-F1 scores over the existing state-of-the-art solutions for the primary task of trustworthy summarization.
%@ 9781450391320 - Sharma, S., Nayak, T., Bose, A., Meena, A. K., Dasgupta, K., Ganguly, N., and Goyal, P. (2022){FinRED}: A Dataset for Relation Extraction in Financial Domain. In Companion Proceedings of the Web Conference 2022, {ACM}.
@inproceedings{Sharma_2022,
author = {Sharma, Soumya and Nayak, Tapas and Bose, Arusarka and Meena, Ajay Kumar and Dasgupta, Koustuv and Ganguly, Niloy and Goyal, Pawan},
booktitle = {Companion Proceedings of the Web Conference 2022},
keywords = {"sys:relevantfor:l3s"},
month = {04},
publisher = {{ACM}},
title = {{FinRED}: A Dataset for Relation Extraction in Financial Domain},
year = 2022
}%0 Conference Paper
%1 Sharma_2022
%A Sharma, Soumya
%A Nayak, Tapas
%A Bose, Arusarka
%A Meena, Ajay Kumar
%A Dasgupta, Koustuv
%A Ganguly, Niloy
%A Goyal, Pawan
%B Companion Proceedings of the Web Conference 2022
%D 2022
%I {ACM}
%R 10.1145/3487553.3524637
%T {FinRED}: A Dataset for Relation Extraction in Financial Domain
%U https://doi.org/10.1145%2F3487553.3524637 - Patro, G. K., Jana, P., Chakraborty, A., Gummadi, K. P., and Ganguly, N. (2022)Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction. In Proceedings of the {ACM} Web Conference 2022, {ACM}.
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author = {Patro, Gourab K. and Jana, Prithwish and Chakraborty, Abhijnan and Gummadi, Krishna P. and Ganguly, Niloy},
booktitle = {Proceedings of the {ACM} Web Conference 2022},
keywords = {"sys:relevantfor:l3s"},
month = {04},
publisher = {{ACM}},
title = {Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction},
year = 2022
}%0 Conference Paper
%1 Patro_2022
%A Patro, Gourab K.
%A Jana, Prithwish
%A Chakraborty, Abhijnan
%A Gummadi, Krishna P.
%A Ganguly, Niloy
%B Proceedings of the {ACM} Web Conference 2022
%D 2022
%I {ACM}
%R 10.1145/3485447.3512136
%T Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction
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author = {Nguyen, Duy and Henschel, Roberto and Rosenhahn, Bodo and Sonntag, Daniel and Swoboda, Paul},
booktitle = {Computer Vision and Pattern Recognition (CVPR)},
keywords = {LMGP},
month = {06},
pages = {1-10},
title = {LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking},
year = 2022
}%0 Conference Paper
%1 NguHen2022
%A Nguyen, Duy
%A Henschel, Roberto
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%A Sonntag, Daniel
%A Swoboda, Paul
%B Computer Vision and Pattern Recognition (CVPR)
%D 2022
%P 1-10
%R 10.1109/CVPR52688.2022.00866
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booktitle = {Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science},
keywords = {leibnizailab},
title = {To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation},
year = 2022
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%1 Stahl_Spliethöver_Wachsmuth
%A Stahl, Maja
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%B Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science
%D 2022
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author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Mohan, Aditya and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
journal = {Arxiv Preprint},
keywords = {leibnizailab},
publisher = {arXiv},
title = {Contextualize Me -- The Case for Context in Reinforcement Learning},
year = 2022
}%0 Journal Article
%1 https://doi.org/10.48550/arxiv.2202.04500
%A Benjamins, Carolin
%A Eimer, Theresa
%A Schubert, Frederik
%A Mohan, Aditya
%A Biedenkapp, André
%A Rosenhahn, Bodo
%A Hutter, Frank
%A Lindauer, Marius
%D 2022
%I arXiv
%J Arxiv Preprint
%R 10.48550/ARXIV.2202.04500
%T Contextualize Me -- The Case for Context in Reinforcement Learning
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%A Hinrichs, Reemt
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title = {Wochenende - modular and flexible alignment-based shotgun metagenome analysis},
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booktitle = {20th World Conference on Non-Destructive Testing (WCNDT 2020)},
keywords = {Lead},
title = {Analysis of the Repeatability of the Pencil Lead Break in Comparison to the Ball Impact and Electromagnetic Body-Noise Actuator},
year = 2022
}%0 Conference Paper
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%A Hinrichs, Reemt
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%A Lange, Alexander
%A Schmidt, Boso
%A Ostermann, J{ö}rn
%A Marx, Steffen
%B 20th World Conference on Non-Destructive Testing (WCNDT 2020)
%D 2022
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month = {01},
number = 2,
title = {Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis},
volume = 8,
year = 2022
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%1 GriKuh2022
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%A Gajdt, Anna
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%J Horticulturae
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%B International Conference on Control, Automation and Diagnosis (ICCAD)
%D 2022
%P 1-6
%R 10.1109/ICCAD55197.2022.9853909
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keywords = {l3s},
title = {Analyzing clinical RNAseq data with machine learning models greatly improves the genetic diagnosis in pediatric acute lymphoblastic leukemia},
year = 2022
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%1 ESHG2022analyzing
%A Tang, Ming
%A Antic, Zeljko
%A Pietzsch, Stefan
%A Lentes, Jana
%A Hofmann, Winfriede
%A Cario, Gunnar
%A Escherich, Gabriele
%A Udo Zu Stadt, Udo
%A Schlegelberger, Brigitte
%A Horstmann, Martin
%A Stanulla, Martin
%A Bergmann, Anke Katharina
%D 2022
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keywords = {Detection},
month = {08},
title = {Detection of impulsive signals on tendons for hybrid wind turbines using acoustic emission measurements},
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%B International Symposium on Non-Destructive Testing in Civil Engineering (NDT-CE 2022)
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keywords = {Clustering},
month = {04},
title = {Constrained Mean Shift Clustering},
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%A Schier, Maximilian
%A Reinders, Christoph
%A Rosenhahn, Bodo
%B Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)
%D 2022
%T Constrained Mean Shift Clustering
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2021
- Koley, P., Saha, A., Bhattacharya, S., Ganguly, N., and De, A. (2021)Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach, ACM Trans. Knowl. Discov. Data, Association for Computing Machinery, New York, NY, USA 15.The networked opinion diffusion in online social networks is often governed by the two genres of opinions—endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.
@article{10.1145/3449361,
abstract = {The networked opinion diffusion in online social networks is often governed by the two genres of opinions—endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.},
address = {New York, NY, USA},
author = {Koley, Paramita and Saha, Avirup and Bhattacharya, Sourangshu and Ganguly, Niloy and De, Abir},
journal = {ACM Trans. Knowl. Discov. Data},
keywords = {leibnizailab},
month = {06},
number = 6,
publisher = {Association for Computing Machinery},
title = {Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach},
volume = 15,
year = 2021
}%0 Journal Article
%1 10.1145/3449361
%A Koley, Paramita
%A Saha, Avirup
%A Bhattacharya, Sourangshu
%A Ganguly, Niloy
%A De, Abir
%C New York, NY, USA
%D 2021
%I Association for Computing Machinery
%J ACM Trans. Knowl. Discov. Data
%N 6
%R 10.1145/3449361
%T Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach
%U https://doi.org/10.1145/3449361
%V 15
%X The networked opinion diffusion in online social networks is often governed by the two genres of opinions—endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors. - Tennakoon, R., Suter, D., Zhang, E., Chin, T.-J., and Bab-Hadiashar, A. (2021)Consensus Maximisation Using Influences of Monotone Boolean Functions. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2865–2874.
@inproceedings{BMFcvpr2021,
author = {Tennakoon, Ruwan and Suter, David and Zhang, Erchuan and Chin, Tat-Jun and Bab-Hadiashar, Alireza},
booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
keywords = {leibnizailab},
note = {Oral Presentation (17\% of accepted papers, roughly 3\% of sumitted papers)},
pages = {2865-2874},
title = {Consensus Maximisation Using Influences of Monotone Boolean Functions},
year = 2021
}%0 Conference Paper
%1 BMFcvpr2021
%A Tennakoon, Ruwan
%A Suter, David
%A Zhang, Erchuan
%A Chin, Tat-Jun
%A Bab-Hadiashar, Alireza
%B 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
%D 2021
%P 2865-2874
%R 10.1109/CVPR46437.2021.00289
%T Consensus Maximisation Using Influences of Monotone Boolean Functions - Pestel-Schiller, U., Hu, K., Gritzner, D., and Ostermann, J. (2021)Determination of Relevant Hyperspectral Bands Using a Spectrally Constrained CNN. In 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Paper 15.
@inproceedings{PesHu2021a,
author = {Pestel-Schiller, Ulrike and Hu, Kai and Gritzner, Daniel and Ostermann, J{ö}rn},
booktitle = {11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Paper 15},
keywords = {Relevant},
month = {03},
title = {Determination of Relevant Hyperspectral Bands Using a Spectrally Constrained CNN},
year = 2021
}%0 Conference Paper
%1 PesHu2021a
%A Pestel-Schiller, Ulrike
%A Hu, Kai
%A Gritzner, Daniel
%A Ostermann, J{ö}rn
%B 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Paper 15
%D 2021
%T Determination of Relevant Hyperspectral Bands Using a Spectrally Constrained CNN - Narisetti, N., Henke, M., Seiler, C., Junker, A., Ostermann, J., Altmann, T., and Gladilin, E. (2021)Fully-automated root image analysis (faRIA), Scientific Reports 11.
@article{NarHen2021,
author = {Narisetti, Narendra and Henke, Michael and Seiler, Christiane and Junker, Astrid and Ostermann, J{ö}rn and Altmann, Thomas and Gladilin, Evgeny},
journal = {Scientific Reports},
keywords = {analysis},
month = {08},
title = {Fully-automated root image analysis (faRIA)},
volume = 11,
year = 2021
}%0 Journal Article
%1 NarHen2021
%A Narisetti, Narendra
%A Henke, Michael
%A Seiler, Christiane
%A Junker, Astrid
%A Ostermann, J{ö}rn
%A Altmann, Thomas
%A Gladilin, Evgeny
%D 2021
%J Scientific Reports
%R https://doi.org/10.1038/s41598-021-95480-y
%T Fully-automated root image analysis (faRIA)
%U https://www.nature.com/articles/s41598-021-95480-y
%V 11 - Guerrero-Viu, J., Hauns, S., Izquierdo, S., Miotto, G., Schrodi, S., Biedenkapp, A., Elsken, T., Deng, D., Lindauer, M., and Hutter, F. (2021)Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. In Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML’21.
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author = {Guerrero-Viu, Julia and Hauns, Sven and Izquierdo, Sergio and Miotto, Guilherme and Schrodi, Simon and Biedenkapp, Andre and Elsken, Thomas and Deng, Difan and Lindauer, Marius and Hutter, Frank},
booktitle = {Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21},
keywords = {Optimization},
month = {07},
title = {Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization},
year = 2021
}%0 Conference Paper
%1 GueHau2021a
%A Guerrero-Viu, Julia
%A Hauns, Sven
%A Izquierdo, Sergio
%A Miotto, Guilherme
%A Schrodi, Simon
%A Biedenkapp, Andre
%A Elsken, Thomas
%A Deng, Difan
%A Lindauer, Marius
%A Hutter, Frank
%B Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21
%D 2021
%T Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
%U https://arxiv.org/abs/2105.01015 - Kluger, F., Ackermann, H., Brachmann, E., Yang, M. Y., and Rosenhahn, B. (2021)Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images. In CVPR.
@inproceedings{KluAck2021a,
author = {Kluger, Florian and Ackermann, Hanno and Brachmann, Eric and Yang, Michael Ying and Rosenhahn, Bodo},
booktitle = {CVPR},
keywords = {Cuboids},
month = {06},
title = {Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images},
year = 2021
}%0 Conference Paper
%1 KluAck2021a
%A Kluger, Florian
%A Ackermann, Hanno
%A Brachmann, Eric
%A Yang, Michael Ying
%A Rosenhahn, Bodo
%B CVPR
%D 2021
%T Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images - Schubert, F., Awiszus, M., and Rosenhahn, B. (2021)TOAD-GAN: a Flexible Framework for Few-Shot Level Generation in Token-Based Games, IEEE Transactions on Games.
@article{SchAwi2021,
author = {Schubert, Frederik and Awiszus, Maren and Rosenhahn, Bodo},
journal = {IEEE Transactions on Games},
keywords = {TOAD-GAN},
month = {03},
note = {10 pages, 14 figures.},
title = {TOAD-GAN: a Flexible Framework for Few-Shot Level Generation in Token-Based Games},
year = 2021
}%0 Journal Article
%1 SchAwi2021
%A Schubert, Frederik
%A Awiszus, Maren
%A Rosenhahn, Bodo
%D 2021
%J IEEE Transactions on Games
%R 10.1109/TG.2021.3069833
%T TOAD-GAN: a Flexible Framework for Few-Shot Level Generation in Token-Based Games
%U https://ieeexplore.ieee.org/document/9390320 - Benjak, M., Meuel, H., Laude, T., and Ostermann, J. (2021)Enhanced Machine Learning-based Inter Coding for VVC. In 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (ICAIIC 2021).
@inproceedings{BenMeu2021,
author = {Benjak, Martin and Meuel, Holger and Laude, Thorsten and Ostermann, J{ö}rn},
booktitle = {2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (ICAIIC 2021)},
keywords = {for},
month = {04},
note = {accepted for publication},
title = {Enhanced Machine Learning-based Inter Coding for VVC},
year = 2021
}%0 Conference Paper
%1 BenMeu2021
%A Benjak, Martin
%A Meuel, Holger
%A Laude, Thorsten
%A Ostermann, J{ö}rn
%B 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (ICAIIC 2021)
%D 2021
%T Enhanced Machine Learning-based Inter Coding for VVC - Dockhorn, A., and Kruse, R. (2021)Modelheuristics for efficient forward model learning, At-Automatisierungstechnik.
@article{DocKru2021a,
author = {Dockhorn, Alexander and Kruse, Rudolf},
journal = {At-Automatisierungstechnik},
keywords = {for},
month = 10,
title = {Modelheuristics for efficient forward model learning},
year = 2021
}%0 Journal Article
%1 DocKru2021a
%A Dockhorn, Alexander
%A Kruse, Rudolf
%D 2021
%J At-Automatisierungstechnik
%R 10.1515/auto-2021-0037
%T Modelheuristics for efficient forward model learning
%U https://www.degruyter.com/document/doi/10.1515/auto-2021-0037/html - Dockhorn, A., Hurtado-Grueso, J., Jeurissen, D., Xu, L., and Perez-Liebana, D. (2021)Portfolio Search and Optimization for General Strategy Game-Playing. In 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 2085–2092.
@inproceedings{DocHur2021,
author = {Dockhorn, Alexander and Hurtado-Grueso, Jorge and Jeurissen, Dominik and Xu, Linjie and Perez-Liebana, Diego},
booktitle = {2021 IEEE Congress on Evolutionary Computation (CEC)},
keywords = {Search},
pages = {2085-2092},
title = {Portfolio Search and Optimization for General Strategy Game-Playing},
year = 2021
}%0 Conference Paper
%1 DocHur2021
%A Dockhorn, Alexander
%A Hurtado-Grueso, Jorge
%A Jeurissen, Dominik
%A Xu, Linjie
%A Perez-Liebana, Diego
%B 2021 IEEE Congress on Evolutionary Computation (CEC)
%D 2021
%P 2085-2092
%R 10.1109/CEC45853.2021.9504824
%T Portfolio Search and Optimization for General Strategy Game-Playing
%U https://ieeexplore.ieee.org/document/9504824 - Ilyas, Z., Sharif, N., Schousboe, J. T., Lewis, J. R., Suter, D., and Gilani, S. Z. (2021)GuideNet: Learning Inter- Vertebral Guides in DXA Lateral Spine Images. In 2021 Digital Image Computing: Techniques and Applications (DICTA), pp. 01–07.
@inproceedings{9647067,
author = {Ilyas, Zaid and Sharif, Naeha and Schousboe, John T. and Lewis, Joshua R. and Suter, David and Gilani, Syed Zulqarnain},
booktitle = {2021 Digital Image Computing: Techniques and Applications (DICTA)},
keywords = {leibnizailab},
pages = {01-07},
title = {GuideNet: Learning Inter- Vertebral Guides in DXA Lateral Spine Images},
year = 2021
}%0 Conference Paper
%1 9647067
%A Ilyas, Zaid
%A Sharif, Naeha
%A Schousboe, John T.
%A Lewis, Joshua R.
%A Suter, David
%A Gilani, Syed Zulqarnain
%B 2021 Digital Image Computing: Techniques and Applications (DICTA)
%D 2021
%P 01-07
%R 10.1109/DICTA52665.2021.9647067
%T GuideNet: Learning Inter- Vertebral Guides in DXA Lateral Spine Images - Pestel-Schiller, U., and Ostermann, J. (2021)Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability. In 13th European Conference on Synthetic Aperture Radar.
@inproceedings{PesOst2021a,
author = {Pestel-Schiller, Ulrike and Ostermann, J{ö}rn},
booktitle = {13th European Conference on Synthetic Aperture Radar},
keywords = {Compressed},
month = {03},
title = {Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability},
year = 2021
}%0 Conference Paper
%1 PesOst2021a
%A Pestel-Schiller, Ulrike
%A Ostermann, J{ö}rn
%B 13th European Conference on Synthetic Aperture Radar
%D 2021
%T Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability - Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2021)CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. In NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning.
@inproceedings{BenEim2021a,
author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
booktitle = {NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning},
keywords = {Reinforcement},
month = 12,
title = {CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning},
year = 2021
}%0 Conference Paper
%1 BenEim2021a
%A Benjamins, Carolin
%A Eimer, Theresa
%A Schubert, Frederik
%A Biedenkapp, André
%A Rosenhahn, Bodo
%A Hutter, Frank
%A Lindauer, Marius
%B NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning
%D 2021
%T CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning - Kaushal, A., Saha, A., and Ganguly, N. (2021)tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets, pp. 3879–3889.The stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. Specifically, a simple tweet classification model achieved human-level performance on the WT–WT dataset and more than two-third accuracy on various other datasets. We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems. Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets.
@proceedings{J,
abstract = {The stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. Specifically, a simple tweet classification model achieved human-level performance on the WT–WT dataset and more than two-third accuracy on various other datasets. We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems. Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets.},
author = {Kaushal, Ayush and Saha, Avirup and Ganguly, Niloy},
keywords = {leibnizailab},
month = {06},
pages = {3879-3889},
title = {tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets},
year = 2021
}%0 Conference Proceedings
%1 J
%A Kaushal, Ayush
%A Saha, Avirup
%A Ganguly, Niloy
%D 2021
%P 3879-3889
%T tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets
%X The stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. Specifically, a simple tweet classification model achieved human-level performance on the WT–WT dataset and more than two-third accuracy on various other datasets. We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems. Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets. - Wu, W., Li, B., Luo, C., and Nejdl, W. (2021)Hashing-Accelerated Graph Neural Networks for Link Prediction. In .Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources.
@conference{wu2021hashingaccelerated,
abstract = {Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources.},
author = {Wu, Wei and Li, Bin and Luo, Chuan and Nejdl, Wolfgang},
keywords = {l3s},
note = {cite arxiv:2105.14280},
title = {Hashing-Accelerated Graph Neural Networks for Link Prediction},
year = 2021
}%0 Generic
%1 wu2021hashingaccelerated
%A Wu, Wei
%A Li, Bin
%A Luo, Chuan
%A Nejdl, Wolfgang
%D 2021
%R 10.1145/3442381.3449884
%T Hashing-Accelerated Graph Neural Networks for Link Prediction
%U http://arxiv.org/abs/2105.14280
%X Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources. - Holzapfel, C., Sag, S., Graf-Schindler, J., Fischer, M., Drabsch, T., Illig, T., Grallert, H., Stecher, L., Strack, C., Caterson, I., Jebb, S., Hauner, H., and Baessler, A. (2021)Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis, Nutrients, MDPI 13.Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated (FTO), transmembrane protein 18 (TMEM18), melanocortin-4 receptor (MC4R), SEC16 homolog B (SEC16B), and brain-derived neurotrophic factor (BDNF) gene are associated with anthropometric changes during behavioural intervention for weight loss. genetic and anthropometric data from 576 individuals with overweight and obesity from four lifestyle interventions were obtained. A genetic predisposition score (GPS) was calculated. Our results show that study participants had a mean age of 48.2 ± 12.6 years and a mean baseline body mass index of 33.9 ± 6.4 kg/m2. Mean weight reduction after 12 months was −7.7 ± 10.9 kg. After 12 months of intervention, the MC4R SNPs rs571312 and rs17782313 were significantly associated with a greater decrease in body weight and BMI (p = 0.012, p = 0.011, respectively). The investigated SNPs within the other four genetic loci showed no statistically significant association with changes in anthropometric parameters. The GPS showed no statistically significant association with weight reduction. In conclusion there was no consistent evidence for statistically significant associations of SNPs with anthropometric changes during a behavioural intervention. It seems that other factors play a more significant in weight management than the investigated SNPs.
@article{holzapfel2021association,
abstract = {Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated (FTO), transmembrane protein 18 (TMEM18), melanocortin-4 receptor (MC4R), SEC16 homolog B (SEC16B), and brain-derived neurotrophic factor (BDNF) gene are associated with anthropometric changes during behavioural intervention for weight loss. genetic and anthropometric data from 576 individuals with overweight and obesity from four lifestyle interventions were obtained. A genetic predisposition score (GPS) was calculated. Our results show that study participants had a mean age of 48.2 ± 12.6 years and a mean baseline body mass index of 33.9 ± 6.4 kg/m2. Mean weight reduction after 12 months was −7.7 ± 10.9 kg. After 12 months of intervention, the MC4R SNPs rs571312 and rs17782313 were significantly associated with a greater decrease in body weight and BMI (p = 0.012, p = 0.011, respectively). The investigated SNPs within the other four genetic loci showed no statistically significant association with changes in anthropometric parameters. The GPS showed no statistically significant association with weight reduction. In conclusion there was no consistent evidence for statistically significant associations of SNPs with anthropometric changes during a behavioural intervention. It seems that other factors play a more significant in weight management than the investigated SNPs.},
author = {Holzapfel, C and Sag, S and Graf-Schindler, J and Fischer, M and Drabsch, T and Illig, T and Grallert, H and Stecher, L and Strack, C and Caterson, ID and Jebb, SA and Hauner, H and Baessler, A},
journal = {Nutrients},
keywords = {l3s},
number = 3,
publisher = {MDPI},
title = {Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis},
type = {Publication},
volume = 13,
year = 2021
}%0 Journal Article
%1 holzapfel2021association
%A Holzapfel, C
%A Sag, S
%A Graf-Schindler, J
%A Fischer, M
%A Drabsch, T
%A Illig, T
%A Grallert, H
%A Stecher, L
%A Strack, C
%A Caterson, ID
%A Jebb, SA
%A Hauner, H
%A Baessler, A
%D 2021
%I MDPI
%J Nutrients
%N 3
%R 10.3390/nu13030819
%T Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis
%U https://ora.ox.ac.uk/objects/uuid:98987391-ba79-4686-a819-5fbe2a79ff45
%V 13
%X Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated (FTO), transmembrane protein 18 (TMEM18), melanocortin-4 receptor (MC4R), SEC16 homolog B (SEC16B), and brain-derived neurotrophic factor (BDNF) gene are associated with anthropometric changes during behavioural intervention for weight loss. genetic and anthropometric data from 576 individuals with overweight and obesity from four lifestyle interventions were obtained. A genetic predisposition score (GPS) was calculated. Our results show that study participants had a mean age of 48.2 ± 12.6 years and a mean baseline body mass index of 33.9 ± 6.4 kg/m2. Mean weight reduction after 12 months was −7.7 ± 10.9 kg. After 12 months of intervention, the MC4R SNPs rs571312 and rs17782313 were significantly associated with a greater decrease in body weight and BMI (p = 0.012, p = 0.011, respectively). The investigated SNPs within the other four genetic loci showed no statistically significant association with changes in anthropometric parameters. The GPS showed no statistically significant association with weight reduction. In conclusion there was no consistent evidence for statistically significant associations of SNPs with anthropometric changes during a behavioural intervention. It seems that other factors play a more significant in weight management than the investigated SNPs. - Hutter, F., Fuks, L., Lindauer, M., and Awad, N. (2021)Method, device and computer program for producing a strategy for a robot.
@article{HutFuk2021,
author = {Hutter, Frank and Fuks, Lior and Lindauer, Marius and Awad, Noor},
keywords = {strategy},
title = {Method, device and computer program for producing a strategy for a robot},
year = 2021
}%0 Journal Article
%1 HutFuk2021
%A Hutter, Frank
%A Fuks, Lior
%A Lindauer, Marius
%A Awad, Noor
%D 2021
%T Method, device and computer program for producing a strategy for a robot
%U https://patentimages.storage.googleapis.com/8b/43/4d/a2876517a8f945/US20210008718A1.pdf - Hao, C., Liao, W., Tang, X., Yang, M. Y., Sester, M., and Rosenhahn, B. (2021)AMENet: Attentive Maps Encoder Network for Trajectory Prediction. In ISPRS Journal of Photogrammetry and Remote Sensing, pp. 253–266.
@inproceedings{CheLia2021b,
author = {Hao, Cheng and Liao, Wentong and Tang, Xuejiao and Yang, Michael Ying and Sester, Monika and Rosenhahn, Bodo},
booktitle = {ISPRS Journal of Photogrammetry and Remote Sensing},
keywords = {AMENet},
pages = {253--266},
title = {AMENet: Attentive Maps Encoder Network for Trajectory Prediction},
volume = 172,
year = 2021
}%0 Conference Paper
%1 CheLia2021b
%A Hao, Cheng
%A Liao, Wentong
%A Tang, Xuejiao
%A Yang, Michael Ying
%A Sester, Monika
%A Rosenhahn, Bodo
%B ISPRS Journal of Photogrammetry and Remote Sensing
%D 2021
%P 253--266
%R https://doi.org/10.1016/j.isprsjprs.2020.12.004
%T AMENet: Attentive Maps Encoder Network for Trajectory Prediction
%U https://doi.org/10.1016/j.isprsjprs.2020.12.004
%V 172 - Schubert, F., Eimer, T., Rosenhahn, B., and Lindauer, M. (2021)Towards Automatic Risk Adaption in Distributional Reinforcement Learning. In Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning (ICML).
@inproceedings{SchEim2021b,
author = {Schubert, Frederik and Eimer, Theresa and Rosenhahn, Bodo and Lindauer, Marius},
booktitle = {Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning (ICML)},
keywords = {Reinforcement},
month = {07},
title = {Towards Automatic Risk Adaption in Distributional Reinforcement Learning},
year = 2021
}%0 Conference Paper
%1 SchEim2021b
%A Schubert, Frederik
%A Eimer, Theresa
%A Rosenhahn, Bodo
%A Lindauer, Marius
%B Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning (ICML)
%D 2021
%T Towards Automatic Risk Adaption in Distributional Reinforcement Learning
%U https://arxiv.org/abs/2106.06317 - Adhisantoso, Y. G. (2021)Verification of the Extension to the Coding of Contact Matrix m58073, ISO/IEC JTC 1/SC 29/WG 8.
@article{Adh2021a,
author = {Adhisantoso, Yeremia Gunawan},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {m58073},
month = 10,
title = {Verification of the Extension to the Coding of Contact Matrix m58073},
year = 2021
}%0 Journal Article
%1 Adh2021a
%A Adhisantoso, Yeremia Gunawan
%D 2021
%J ISO/IEC JTC 1/SC 29/WG 8
%T Verification of the Extension to the Coding of Contact Matrix m58073 - Becker, M., Strengert, M., Junker, D., Kaiser, P. D., Kerrinnes, T., Traenkle, B., Dinter, H., Häring, J., Ghozzi, S., Zeck, A., Weise, F., Peter, A., Hörber, S., Fink, S., Ruoff, F., Dulovic, A., Bakchoul, T., Baillot, A., Lohse, S., Cornberg, M., Illig, T., Gottlieb, J., Smola, S., Karch, A., Berger, K., Rammensee, H.-G., Schenke-Layland, K., Nelde, A., Märklin, M., Heitmann, J. S., Walz, J. S., Templin, M., Joos, T. O., Rothbauer, U., Krause, G., and Schneiderhan-Marra, N. (2021)Exploring beyond clinical routine SARS-CoV-2 serology using MultiCoV-Ab to evaluate endemic coronavirus cross-reactivity, Nat Commun 12.The humoral immune response to SARS-CoV-2 is a benchmark for immunity and detailed analysis is required to understand the manifestation and progression of COVID-19, monitor seroconversion within the general population, and support vaccine development. The majority of currently available commercial serological assays only quantify the SARS-CoV-2 antibody response against individual antigens, limiting our understanding of the immune response. To overcome this, we have developed a multiplex immunoassay (MultiCoV-Ab) including spike and nucleocapsid proteins of SARS-CoV-2 and the endemic human coronaviruses. Compared to three broadly used commercial in vitro diagnostic tests, our MultiCoV-Ab achieves a higher sensitivity and specificity when analyzing a well-characterized sample set of SARS-CoV-2 infected and uninfected individuals. We find a high response against endemic coronaviruses in our sample set, but no consistent cross-reactive IgG response patterns against SARS-CoV-2. Here we show a robust, high-content-enabled, antigen-saving multiplex assay suited to both monitoring vaccination studies and facilitating epidemiologic screenings for humoral immunity towards pandemic and endemic coronaviruses.
@article{noauthororeditor,
abstract = {The humoral immune response to SARS-CoV-2 is a benchmark for immunity and detailed analysis is required to understand the manifestation and progression of COVID-19, monitor seroconversion within the general population, and support vaccine development. The majority of currently available commercial serological assays only quantify the SARS-CoV-2 antibody response against individual antigens, limiting our understanding of the immune response. To overcome this, we have developed a multiplex immunoassay (MultiCoV-Ab) including spike and nucleocapsid proteins of SARS-CoV-2 and the endemic human coronaviruses. Compared to three broadly used commercial in vitro diagnostic tests, our MultiCoV-Ab achieves a higher sensitivity and specificity when analyzing a well-characterized sample set of SARS-CoV-2 infected and uninfected individuals. We find a high response against endemic coronaviruses in our sample set, but no consistent cross-reactive IgG response patterns against SARS-CoV-2. Here we show a robust, high-content-enabled, antigen-saving multiplex assay suited to both monitoring vaccination studies and facilitating epidemiologic screenings for humoral immunity towards pandemic and endemic coronaviruses.},
author = {Becker, Matthias and Strengert, Monika and Junker, Daniel and Kaiser, Philipp D. and Kerrinnes, Tobias and Traenkle, Bjoern and Dinter, Heiko and Häring, Julia and Ghozzi, Stéphane and Zeck, Anne and Weise, Frank and Peter, Andreas and Hörber, Sebastian and Fink, Simon and Ruoff, Felix and Dulovic, Alex and Bakchoul, Tamam and Baillot, Armin and Lohse, Stefan and Cornberg, Markus and Illig, Thomas and Gottlieb, Jens and Smola, Sigrun and Karch, André and Berger, Klaus and Rammensee, Hans-Georg and Schenke-Layland, Katja and Nelde, Annika and Märklin, Melanie and Heitmann, Jonas S. and Walz, Juliane S. and Templin, Markus and Joos, Thomas O. and Rothbauer, Ulrich and Krause, Gérard and Schneiderhan-Marra, Nicole},
journal = {Nat Commun},
keywords = {l3s},
month = {02},
number = 1152,
title = {Exploring beyond clinical routine SARS-CoV-2 serology using MultiCoV-Ab to evaluate endemic coronavirus cross-reactivity},
volume = 12,
year = 2021
}%0 Journal Article
%1 noauthororeditor
%A Becker, Matthias
%A Strengert, Monika
%A Junker, Daniel
%A Kaiser, Philipp D.
%A Kerrinnes, Tobias
%A Traenkle, Bjoern
%A Dinter, Heiko
%A Häring, Julia
%A Ghozzi, Stéphane
%A Zeck, Anne
%A Weise, Frank
%A Peter, Andreas
%A Hörber, Sebastian
%A Fink, Simon
%A Ruoff, Felix
%A Dulovic, Alex
%A Bakchoul, Tamam
%A Baillot, Armin
%A Lohse, Stefan
%A Cornberg, Markus
%A Illig, Thomas
%A Gottlieb, Jens
%A Smola, Sigrun
%A Karch, André
%A Berger, Klaus
%A Rammensee, Hans-Georg
%A Schenke-Layland, Katja
%A Nelde, Annika
%A Märklin, Melanie
%A Heitmann, Jonas S.
%A Walz, Juliane S.
%A Templin, Markus
%A Joos, Thomas O.
%A Rothbauer, Ulrich
%A Krause, Gérard
%A Schneiderhan-Marra, Nicole
%D 2021
%J Nat Commun
%N 1152
%R https://doi.org/10.1038/s41467-021-20973-3
%T Exploring beyond clinical routine SARS-CoV-2 serology using MultiCoV-Ab to evaluate endemic coronavirus cross-reactivity
%V 12
%X The humoral immune response to SARS-CoV-2 is a benchmark for immunity and detailed analysis is required to understand the manifestation and progression of COVID-19, monitor seroconversion within the general population, and support vaccine development. The majority of currently available commercial serological assays only quantify the SARS-CoV-2 antibody response against individual antigens, limiting our understanding of the immune response. To overcome this, we have developed a multiplex immunoassay (MultiCoV-Ab) including spike and nucleocapsid proteins of SARS-CoV-2 and the endemic human coronaviruses. Compared to three broadly used commercial in vitro diagnostic tests, our MultiCoV-Ab achieves a higher sensitivity and specificity when analyzing a well-characterized sample set of SARS-CoV-2 infected and uninfected individuals. We find a high response against endemic coronaviruses in our sample set, but no consistent cross-reactive IgG response patterns against SARS-CoV-2. Here we show a robust, high-content-enabled, antigen-saving multiplex assay suited to both monitoring vaccination studies and facilitating epidemiologic screenings for humoral immunity towards pandemic and endemic coronaviruses. - Bellinghausen, C., Pletz, M. W., Rupp, J., Witzenrath, M., Welsch, C., Zeuzem, S., Trebicka, J., Rohde, G. G. U., and of the CAPNETZ study group, M. (2021)Chronic liver disease negatively affects outcome in hospitalised patients with community-acquired pneumonia, Gut 70, 221–222.
@article{noauthororeditor,
author = {Bellinghausen, Carla and Pletz, Mathias W. and Rupp, Jan and Witzenrath, Martin and Welsch, Christoph and Zeuzem, Stefan and Trebicka, Jonel and Rohde, Gernot G. U. and of the CAPNETZ study group, Members},
journal = {Gut},
keywords = {l3s},
month = {01},
number = 1,
pages = {221-222},
title = {Chronic liver disease negatively affects outcome in hospitalised patients with community-acquired pneumonia},
volume = 70,
year = 2021
}%0 Journal Article
%1 noauthororeditor
%A Bellinghausen, Carla
%A Pletz, Mathias W.
%A Rupp, Jan
%A Witzenrath, Martin
%A Welsch, Christoph
%A Zeuzem, Stefan
%A Trebicka, Jonel
%A Rohde, Gernot G. U.
%A of the CAPNETZ study group, Members
%D 2021
%J Gut
%N 1
%P 221-222
%R doi: 10.1136/gutjnl-2020-320876
%T Chronic liver disease negatively affects outcome in hospitalised patients with community-acquired pneumonia
%V 70 - Rumberg, L., Ehlert, H., L{ü}dtke, U., and Ostermann, J. (2021)Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning. In Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association.
@inproceedings{RumEhl2021,
author = {Rumberg, Lars and Ehlert, Hanna and L{ü}dtke, Ulrike and Ostermann, J{ö}rn},
booktitle = {Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association},
keywords = {Recognition},
month = {08},
title = {Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning},
year = 2021
}%0 Conference Paper
%1 RumEhl2021
%A Rumberg, Lars
%A Ehlert, Hanna
%A L{ü}dtke, Ulrike
%A Ostermann, J{ö}rn
%B Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association
%D 2021
%T Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning - Hachmann, H., Kr{ü}ger, B., Rosenhahn, B., and Nogueira, W. (2021)Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation. In International Symposium on Biomedical Imaging, ISBI.
@inproceedings{HacKru2021a,
author = {Hachmann, Hendrik and Kr{ü}ger, Benjamin and Rosenhahn, Bodo and Nogueira, Waldo},
booktitle = {International Symposium on Biomedical Imaging, ISBI},
keywords = {Computed},
month = {04},
title = {Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation},
year = 2021
}%0 Conference Paper
%1 HacKru2021a
%A Hachmann, Hendrik
%A Kr{ü}ger, Benjamin
%A Rosenhahn, Bodo
%A Nogueira, Waldo
%B International Symposium on Biomedical Imaging, ISBI
%D 2021
%T Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation
%U https://arxiv.org/abs/2103.10434 - Warnstorf, D., Bawadi, R., Schienke, A., Strasser, R., Schmidt, G., Illig, T., Tauscher, M., Thol, F., Heuser, M., Steinemann, D., Davenport, C., Schlegelberger, B., Behrens, Y. L., and Göhring, G. (2021)Unbalanced translocation der(5;17) resulting in a TP53 loss as recurrent aberration in myelodysplastic syndrome and acute myeloid leukemia with complex karyotype, Genes Chromosomes Cancer 60, 452–457.A complex karyotype, detected in myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML), is associated with a reduced median survival. The most frequent chromosomal aberrations in complex karyotypes are deletions of 5q and 17p harboring the tumor suppressor gene TP53. The unbalanced translocation der(5;17) involving chromosome 5q and 17p is a recurrent aberration in MDS/AML, resulting in TP53 loss. We analyzed the karyotypes of 178 patients with an unbalanced translocation der(5;17) using fluorescence R−/G-banding analysis. Whenever possible, fluorescence in situ hybridization (FISH) (n = 138/141), multicolor FISH (n = 8), telomere length measurement (n = 9), targeted DNA sequencing (n = 13), array-CGH (n = 7) and targeted RNA sequencing (n = 2) were conducted. The der(5;17) aberration was accompanied with loss of genetic material in 7q (53%), −7 (27%), gain of 21q (29%), +8 (17%) and − 18 (16%) and all analyzed patients (n = 13) showed a (likely) pathogenic variant inTP53. The der(5;17) cohort showed significantly shortened telomeres in comparison to the healthy age-matched controls (P < .05), but there was no significant telomere shortening in comparison to MDS/AML patients with a complex karyotype without der(5;17). No fusion genes resulted from the unbalanced translocation. This study demonstrates that the unbalanced translocation der(5;17) is associated with a biallelic inactivation of TP53 due to a deletion of TP53 in one allele and a pathogenic variant of the second TP53 allele. Since the breakpoints are located within (near-) heterochromatic regions, alterations of DNA methylation or histone modifications may be involved in the generation of der(5;17).
@article{unbalanced,
abstract = {A complex karyotype, detected in myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML), is associated with a reduced median survival. The most frequent chromosomal aberrations in complex karyotypes are deletions of 5q and 17p harboring the tumor suppressor gene TP53. The unbalanced translocation der(5;17) involving chromosome 5q and 17p is a recurrent aberration in MDS/AML, resulting in TP53 loss. We analyzed the karyotypes of 178 patients with an unbalanced translocation der(5;17) using fluorescence R−/G-banding analysis. Whenever possible, fluorescence in situ hybridization (FISH) (n = 138/141), multicolor FISH (n = 8), telomere length measurement (n = 9), targeted DNA sequencing (n = 13), array-CGH (n = 7) and targeted RNA sequencing (n = 2) were conducted. The der(5;17) aberration was accompanied with loss of genetic material in 7q (53%), −7 (27%), gain of 21q (29%), +8 (17%) and − 18 (16%) and all analyzed patients (n = 13) showed a (likely) pathogenic variant inTP53. The der(5;17) cohort showed significantly shortened telomeres in comparison to the healthy age-matched controls (P < .05), but there was no significant telomere shortening in comparison to MDS/AML patients with a complex karyotype without der(5;17). No fusion genes resulted from the unbalanced translocation. This study demonstrates that the unbalanced translocation der(5;17) is associated with a biallelic inactivation of TP53 due to a deletion of TP53 in one allele and a pathogenic variant of the second TP53 allele. Since the breakpoints are located within (near-) heterochromatic regions, alterations of DNA methylation or histone modifications may be involved in the generation of der(5;17).},
author = {Warnstorf, Daria and Bawadi, Randa and Schienke, Andrea and Strasser, Renate and Schmidt, Gunnar and Illig, Thomas and Tauscher, Marcel and Thol, Felicitas and Heuser, Michael and Steinemann, Doris and Davenport, Claudia and Schlegelberger, Brigitte and Behrens, Yvonne Lisa and Göhring, Gudrun},
journal = {Genes Chromosomes Cancer},
keywords = {l3s},
month = {01},
number = 6,
pages = {452-457},
title = {Unbalanced translocation der(5;17) resulting in a TP53 loss as recurrent aberration in myelodysplastic syndrome and acute myeloid leukemia with complex karyotype},
volume = 60,
year = 2021
}%0 Journal Article
%1 unbalanced
%A Warnstorf, Daria
%A Bawadi, Randa
%A Schienke, Andrea
%A Strasser, Renate
%A Schmidt, Gunnar
%A Illig, Thomas
%A Tauscher, Marcel
%A Thol, Felicitas
%A Heuser, Michael
%A Steinemann, Doris
%A Davenport, Claudia
%A Schlegelberger, Brigitte
%A Behrens, Yvonne Lisa
%A Göhring, Gudrun
%D 2021
%J Genes Chromosomes Cancer
%N 6
%P 452-457
%R https://doi.org/10.1002/gcc.22938
%T Unbalanced translocation der(5;17) resulting in a TP53 loss as recurrent aberration in myelodysplastic syndrome and acute myeloid leukemia with complex karyotype
%V 60
%X A complex karyotype, detected in myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML), is associated with a reduced median survival. The most frequent chromosomal aberrations in complex karyotypes are deletions of 5q and 17p harboring the tumor suppressor gene TP53. The unbalanced translocation der(5;17) involving chromosome 5q and 17p is a recurrent aberration in MDS/AML, resulting in TP53 loss. We analyzed the karyotypes of 178 patients with an unbalanced translocation der(5;17) using fluorescence R−/G-banding analysis. Whenever possible, fluorescence in situ hybridization (FISH) (n = 138/141), multicolor FISH (n = 8), telomere length measurement (n = 9), targeted DNA sequencing (n = 13), array-CGH (n = 7) and targeted RNA sequencing (n = 2) were conducted. The der(5;17) aberration was accompanied with loss of genetic material in 7q (53%), −7 (27%), gain of 21q (29%), +8 (17%) and − 18 (16%) and all analyzed patients (n = 13) showed a (likely) pathogenic variant inTP53. The der(5;17) cohort showed significantly shortened telomeres in comparison to the healthy age-matched controls (P < .05), but there was no significant telomere shortening in comparison to MDS/AML patients with a complex karyotype without der(5;17). No fusion genes resulted from the unbalanced translocation. This study demonstrates that the unbalanced translocation der(5;17) is associated with a biallelic inactivation of TP53 due to a deletion of TP53 in one allele and a pathogenic variant of the second TP53 allele. Since the breakpoints are located within (near-) heterochromatic regions, alterations of DNA methylation or histone modifications may be involved in the generation of der(5;17). - Gritzner, D., Hinrichs, H., Stetter, C., Wielert, H., Breitner, M. H., and Ostermann, J. (2021)Wind Turbine Localization in Satellite and Aerial Images. In Proceedings of the Wind Energy Science Conference 2021, pp. 40–41.
@inproceedings{GriHin2021,
author = {Gritzner, Daniel and Hinrichs, Hauke and Stetter, Chris and Wielert, Henrik and Breitner, Michael H. and Ostermann, J{ö}rn},
booktitle = {Proceedings of the Wind Energy Science Conference 2021},
keywords = {Wind},
number = 10,
pages = {40-41},
title = {Wind Turbine Localization in Satellite and Aerial Images},
year = 2021
}%0 Conference Paper
%1 GriHin2021
%A Gritzner, Daniel
%A Hinrichs, Hauke
%A Stetter, Chris
%A Wielert, Henrik
%A Breitner, Michael H.
%A Ostermann, J{ö}rn
%B Proceedings of the Wind Energy Science Conference 2021
%D 2021
%N 10
%P 40-41
%T Wind Turbine Localization in Satellite and Aerial Images - Cong, Y., Liao, W., Ackermann, H., Yang, M. Y., and Rosenhahn, B. (2021)Spatial-Temporal Transformer for Dynamic Scene Graph Generation. In International Conference on Computer Vision (ICCV).
@inproceedings{ConLia2021a,
author = {Cong, Yuren and Liao, Wentong and Ackermann, Hanno and Yang, Michael Yang and Rosenhahn, Bodo},
booktitle = {International Conference on Computer Vision (ICCV)},
keywords = {Generation},
month = {07},
title = {Spatial-Temporal Transformer for Dynamic Scene Graph Generation},
year = 2021
}%0 Conference Paper
%1 ConLia2021a
%A Cong, Yuren
%A Liao, Wentong
%A Ackermann, Hanno
%A Yang, Michael Yang
%A Rosenhahn, Bodo
%B International Conference on Computer Vision (ICCV)
%D 2021
%T Spatial-Temporal Transformer for Dynamic Scene Graph Generation
%U https://arxiv.org/abs/2107.12309 - Speck, D., Biedenkapp, A., Hutter, F., Mattm{ü}ller, R., and Lindauer, M. (2021)Learning Heuristic Selection with Dynamic Algorithm Configuration. In Proceedings of the 31st International Conference on Automated Planning and Scheduling {(ICAPS’21)}.
@inproceedings{SpeBie2021,
author = {Speck, David and Biedenkapp, André and Hutter, Frank and Mattm{ü}ller, Robert and Lindauer, Marius},
booktitle = {Proceedings of the 31st International Conference on Automated Planning and Scheduling {(ICAPS'21)}},
keywords = {Heuristic},
month = {08},
title = {Learning Heuristic Selection with Dynamic Algorithm Configuration},
year = 2021
}%0 Conference Paper
%1 SpeBie2021
%A Speck, David
%A Biedenkapp, André
%A Hutter, Frank
%A Mattm{ü}ller, Robert
%A Lindauer, Marius
%B Proceedings of the 31st International Conference on Automated Planning and Scheduling {(ICAPS'21)}
%D 2021
%T Learning Heuristic Selection with Dynamic Algorithm Configuration
%U https://arxiv.org/abs/2006.08246 - Xue, Y., Kudenko, D., and Khosla, M. (2021)Graph Learning based Generation of Abstractions for Reinforcement Learning. In Adaptive and Learning Agents Workshop at AAMAS 2021.The application of Reinforcement Learning (RL) Algorithms is often hindered by the combinatorial explosion of the state space. Previous works have leveraged abstractions which condense large state spaces to find tractable solutions, however they assumed that the abstractions are provided by a domain expert. In this work we propose a new approach to automatically construct Abstract Markov Decision Processes (AMDPs) for Potential Based Reward Shaping to improve the sample efficiency of RL algorithms. Our approach to construct abstract states is inspired by graph representation learning methods and effectively encodes topological and reward structure of the ground level MDP. We perform large scale quantitative experiments on Flag Collection domain. We show improvements of up to 6.5 times in sample efficiency and up to 3 times in run time over the baseline approach. Besides, with our qualitative analyses of the generated AMDP we demonstrate the capability of our approach to preserve topological and reward structure of the ground level MDP.
@inproceedings{yuanxue2021graph,
abstract = {The application of Reinforcement Learning (RL) Algorithms is often hindered by the combinatorial explosion of the state space. Previous works have leveraged abstractions which condense large state spaces to find tractable solutions, however they assumed that the abstractions are provided by a domain expert. In this work we propose a new approach to automatically construct Abstract Markov Decision Processes (AMDPs) for Potential Based Reward Shaping to improve the sample efficiency of RL algorithms. Our approach to construct abstract states is inspired by graph representation learning methods and effectively encodes topological and reward structure of the ground level MDP. We perform large scale quantitative experiments on Flag Collection domain. We show improvements of up to 6.5 times in sample efficiency and up to 3 times in run time over the baseline approach. Besides, with our qualitative analyses of the generated AMDP we demonstrate the capability of our approach to preserve topological and reward structure of the ground level MDP.},
author = {Xue, Yuan and Kudenko, Daniel and Khosla, Megha},
booktitle = {Adaptive and Learning Agents Workshop at AAMAS 2021},
keywords = {leibnizailab},
title = {Graph Learning based Generation of Abstractions for Reinforcement Learning},
year = 2021
}%0 Conference Paper
%1 yuanxue2021graph
%A Xue, Yuan
%A Kudenko, Daniel
%A Khosla, Megha
%B Adaptive and Learning Agents Workshop at AAMAS 2021
%D 2021
%T Graph Learning based Generation of Abstractions for Reinforcement Learning
%U https://ala2021.vub.ac.be/papers/ALA2021_paper_57.pdf
%X The application of Reinforcement Learning (RL) Algorithms is often hindered by the combinatorial explosion of the state space. Previous works have leveraged abstractions which condense large state spaces to find tractable solutions, however they assumed that the abstractions are provided by a domain expert. In this work we propose a new approach to automatically construct Abstract Markov Decision Processes (AMDPs) for Potential Based Reward Shaping to improve the sample efficiency of RL algorithms. Our approach to construct abstract states is inspired by graph representation learning methods and effectively encodes topological and reward structure of the ground level MDP. We perform large scale quantitative experiments on Flag Collection domain. We show improvements of up to 6.5 times in sample efficiency and up to 3 times in run time over the baseline approach. Besides, with our qualitative analyses of the generated AMDP we demonstrate the capability of our approach to preserve topological and reward structure of the ground level MDP. - Roy, S., Sural, S., Chhaya, N., Natarajan, A., and Ganguly, N. (2021)An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendation., ACM Trans. Web 15, 9:1–9:25.
@article{journals/corr/abs-2011-09754,
author = {Roy, Soumyadeep and Sural, Shamik and Chhaya, Niyati and Natarajan, Anandhavelu and Ganguly, Niloy},
journal = {ACM Trans. Web},
keywords = {leibnizailab},
number = 2,
pages = {9:1-9:25},
title = {An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendation.},
volume = 15,
year = 2021
}%0 Journal Article
%1 journals/corr/abs-2011-09754
%A Roy, Soumyadeep
%A Sural, Shamik
%A Chhaya, Niyati
%A Natarajan, Anandhavelu
%A Ganguly, Niloy
%D 2021
%J ACM Trans. Web
%N 2
%P 9:1-9:25
%T An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendation.
%U http://dblp.uni-trier.de/db/journals/corr/corr2011.html#abs-2011-09754
%V 15 - Souza, A., Nardi, L., Oliveira, L., Olukotun, K., Lindauer, M., and Hutter, F. (2021)Bayesian Optimization with a Prior for the Optimum. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD).
@inproceedings{SouNar2021a,
author = {Souza, Artur and Nardi, Luigi and Oliveira, Leonardo and Olukotun, Kunle and Lindauer, Marius and Hutter, Frank},
booktitle = {Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)},
keywords = {Optimization},
month = {09},
note = {To appear},
title = {Bayesian Optimization with a Prior for the Optimum},
year = 2021
}%0 Conference Paper
%1 SouNar2021a
%A Souza, Artur
%A Nardi, Luigi
%A Oliveira, Leonardo
%A Olukotun, Kunle
%A Lindauer, Marius
%A Hutter, Frank
%B Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)
%D 2021
%T Bayesian Optimization with a Prior for the Optimum
%U https://arxiv.org/abs/2006.14608 - Dong, T., Brogden, G., Gerold, G., and Khosla, M. (2021)A multitask transfer learning framework for the prediction of virus-human protein-protein interactions, BMC Bioinformatics 22, 572.
@article{dong2021multitask,
author = {Dong, TN and Brogden, G and Gerold, G and Khosla, M},
journal = {BMC Bioinformatics},
keywords = {leibnizailab},
number = 1,
pages = 572,
title = {A multitask transfer learning framework for the prediction of virus-human protein-protein interactions},
volume = 22,
year = 2021
}%0 Journal Article
%1 dong2021multitask
%A Dong, TN
%A Brogden, G
%A Gerold, G
%A Khosla, M
%D 2021
%J BMC Bioinformatics
%N 1
%P 572
%R 10.1186/s12859-021-04484-y
%T A multitask transfer learning framework for the prediction of virus-human protein-protein interactions
%U /brokenurl#pubmedurl = {https://pubmed.ncbi.nlm.nih.gov/34837942/}
%V 22 - Truong, G., Le, H., Suter, D., Zhang, E., and Gilani, S. Z. (2021)Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10343–10352.
@inproceedings{giang_cvpr2021,
author = {Truong, Giang and Le, Huu and Suter, David and Zhang, Erchuan and Gilani, Syed Zulqarnain},
booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
keywords = {leibnizailab},
pages = {10343-10352},
title = {Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach},
year = 2021
}%0 Conference Paper
%1 giang_cvpr2021
%A Truong, Giang
%A Le, Huu
%A Suter, David
%A Zhang, Erchuan
%A Gilani, Syed Zulqarnain
%B 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
%D 2021
%P 10343-10352
%R 10.1109/CVPR46437.2021.01021
%T Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach - Sheshadri, S., Saha, A., Patel, P., Datta, S., and Ganguly, N. (2021)Graph-based semi-supervised learning through the lens of safety. In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (de Campos, C., and Maathuis, M. H., Eds.), pp. 1576–1586, PMLR.Graph-based semi-supervised learning (G-SSL) algorithms have witnessed rapid development and widespread usage across a variety of applications in recent years. However, the theoretical characterisation of the efficacy of such algorithms has remained an under-explored area. We introduce a novel algorithm for G-SSL, CSX, whose objective function extends those of Label Propagation and Expander, two popular G-SSL algorithms. We provide data-dependent generalisation error bounds for all three aforementioned algorithms when they are applied to graphs drawn from a partially labelled extension of a versatile latent space graph generative model. The bounds we obtain enable us to characterise the predictive performance as measured by accuracy in terms of homophily and label quantity. Building on this we develop a key notion of GLM-safety which enables us to compare G-SSL algorithms on the basis of the range of graphs on which they obtain a guaranteed accuracy. We show that the proposed algorithm CSX has a better GLM-safety profile than Label Propagation and Expander while achieving comparable or better accuracy on synthetic as well as real-world benchmark networks.
@inproceedings{pmlr-v161-sheshadri21a,
abstract = {Graph-based semi-supervised learning (G-SSL) algorithms have witnessed rapid development and widespread usage across a variety of applications in recent years. However, the theoretical characterisation of the efficacy of such algorithms has remained an under-explored area. We introduce a novel algorithm for G-SSL, CSX, whose objective function extends those of Label Propagation and Expander, two popular G-SSL algorithms. We provide data-dependent generalisation error bounds for all three aforementioned algorithms when they are applied to graphs drawn from a partially labelled extension of a versatile latent space graph generative model. The bounds we obtain enable us to characterise the predictive performance as measured by accuracy in terms of homophily and label quantity. Building on this we develop a key notion of GLM-safety which enables us to compare G-SSL algorithms on the basis of the range of graphs on which they obtain a guaranteed accuracy. We show that the proposed algorithm CSX has a better GLM-safety profile than Label Propagation and Expander while achieving comparable or better accuracy on synthetic as well as real-world benchmark networks.},
author = {Sheshadri, Shreyas and Saha, Avirup and Patel, Priyank and Datta, Samik and Ganguly, Niloy},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
editor = {de Campos, Cassio and Maathuis, Marloes H.},
keywords = {leibnizailab},
month = {27--30 Jul},
pages = {1576--1586},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {Graph-based semi-supervised learning through the lens of safety},
volume = 161,
year = 2021
}%0 Conference Paper
%1 pmlr-v161-sheshadri21a
%A Sheshadri, Shreyas
%A Saha, Avirup
%A Patel, Priyank
%A Datta, Samik
%A Ganguly, Niloy
%B Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence
%D 2021
%E de Campos, Cassio
%E Maathuis, Marloes H.
%I PMLR
%P 1576--1586
%T Graph-based semi-supervised learning through the lens of safety
%U https://proceedings.mlr.press/v161/sheshadri21a.html
%V 161
%X Graph-based semi-supervised learning (G-SSL) algorithms have witnessed rapid development and widespread usage across a variety of applications in recent years. However, the theoretical characterisation of the efficacy of such algorithms has remained an under-explored area. We introduce a novel algorithm for G-SSL, CSX, whose objective function extends those of Label Propagation and Expander, two popular G-SSL algorithms. We provide data-dependent generalisation error bounds for all three aforementioned algorithms when they are applied to graphs drawn from a partially labelled extension of a versatile latent space graph generative model. The bounds we obtain enable us to characterise the predictive performance as measured by accuracy in terms of homophily and label quantity. Building on this we develop a key notion of GLM-safety which enables us to compare G-SSL algorithms on the basis of the range of graphs on which they obtain a guaranteed accuracy. We show that the proposed algorithm CSX has a better GLM-safety profile than Label Propagation and Expander while achieving comparable or better accuracy on synthetic as well as real-world benchmark networks. - Wandt, B., Rudolph, M., Zell, P., Rhodin, H., and Rosenhahn, B. (2021)CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild. In Computer Vision and Pattern Recognition (CVPR).
@inproceedings{WanRud2021a,
author = {Wandt, Bastian and Rudolph, Marco and Zell, Petrissa and Rhodin, Helge and Rosenhahn, Bodo},
booktitle = {Computer Vision and Pattern Recognition (CVPR)},
keywords = {l3s},
month = {06},
title = {CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild},
year = 2021
}%0 Conference Paper
%1 WanRud2021a
%A Wandt, Bastian
%A Rudolph, Marco
%A Zell, Petrissa
%A Rhodin, Helge
%A Rosenhahn, Bodo
%B Computer Vision and Pattern Recognition (CVPR)
%D 2021
%T CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild - Hinrichs, R., Schmidt, A., Koslowski, J., Ostermann, J., and Denkena, B. (2021)Analysis of the impact of data compression on condition monitoring algorithms for ball screws. In CMMO CIRP 2021.
@inproceedings{HinSch2021,
author = {Hinrichs, Reemt and Schmidt, Alexander and Koslowski, Julian and Ostermann, J{ö}rn and Denkena, Berend},
booktitle = {CMMO CIRP 2021},
keywords = {screws},
title = {Analysis of the impact of data compression on condition monitoring algorithms for ball screws},
year = 2021
}%0 Conference Paper
%1 HinSch2021
%A Hinrichs, Reemt
%A Schmidt, Alexander
%A Koslowski, Julian
%A Ostermann, J{ö}rn
%A Denkena, Berend
%B CMMO CIRP 2021
%D 2021
%T Analysis of the impact of data compression on condition monitoring algorithms for ball screws - Eggensperger, K., M{ü}ller, P., Mallik, N., Feurer, M., Sass, R., Klein, A., Awad, N., Lindauer, M., and Hutter, F. (2021)HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track).
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author = {Eggensperger, Katharina and M{ü}ller, Philipp and Mallik, Neeratyoy and Feurer, Matthias and Sass, René and Klein, Aaron and Awad, Noor and Lindauer, Marius and Hutter, Frank},
booktitle = {Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track)},
keywords = {HPOBench},
month = 12,
title = {HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO},
year = 2021
}%0 Conference Paper
%1 EggMue2021
%A Eggensperger, Katharina
%A M{ü}ller, Philipp
%A Mallik, Neeratyoy
%A Feurer, Matthias
%A Sass, René
%A Klein, Aaron
%A Awad, Noor
%A Lindauer, Marius
%A Hutter, Frank
%B Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track)
%D 2021
%T HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
%U https://arxiv.org/abs/2109.06716 - Benjak, M., Samayoa, Y., and Ostermann, J. (2021)Neural Network Based Error Concealment for VVC. In Proceedings of the 28th IEEE International Conference on Image Processing (ICIP).
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author = {Benjak, Martin and Samayoa, Yasser and Ostermann, J{ö}rn},
booktitle = {Proceedings of the 28th IEEE International Conference on Image Processing (ICIP)},
keywords = {Neural},
month = {09},
note = {accepted for publication},
title = {Neural Network Based Error Concealment for VVC},
year = 2021
}%0 Conference Paper
%1 BenSam2021a
%A Benjak, Martin
%A Samayoa, Yasser
%A Ostermann, J{ö}rn
%B Proceedings of the 28th IEEE International Conference on Image Processing (ICIP)
%D 2021
%T Neural Network Based Error Concealment for VVC - Kellermann, C., Adhisantoso, Y. G., Munderloh, M., and Ostermann, J. (2021)Introduction to an Adaptive Remaining Useful Life Prediction for forming tools (accepted). In Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).
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author = {Kellermann, Christoph and Adhisantoso, Yeramia Gunawan and Munderloh, Marco and Ostermann, J{ö}rn},
booktitle = {Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)},
keywords = {Remaining},
month = {07},
title = {Introduction to an Adaptive Remaining Useful Life Prediction for forming tools (accepted)},
year = 2021
}%0 Conference Paper
%1 KelGun2021a
%A Kellermann, Christoph
%A Adhisantoso, Yeramia Gunawan
%A Munderloh, Marco
%A Ostermann, J{ö}rn
%B Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
%D 2021
%T Introduction to an Adaptive Remaining Useful Life Prediction for forming tools (accepted) - Hornakova*, A., Kaiser*, T., Rosenhahn, B., Swoboda, P., Henschel, R., and equal contribution), (*. (2021)Higher Order Multiple Object Tracking for Crowded Scenes, Computer Vision and Pattern Recognition Workshops (CVPRW).
@article{HorKai2021a,
author = {Hornakova*, Andrea and Kaiser*, Timo and Rosenhahn, Bodo and Swoboda, Paul and Henschel, Roberto and equal contribution), (*},
journal = {Computer Vision and Pattern Recognition Workshops (CVPRW)},
keywords = {Object},
month = {06},
title = {Higher Order Multiple Object Tracking for Crowded Scenes},
year = 2021
}%0 Journal Article
%1 HorKai2021a
%A Hornakova*, Andrea
%A Kaiser*, Timo
%A Rosenhahn, Bodo
%A Swoboda, Paul
%A Henschel, Roberto
%A equal contribution), (*
%D 2021
%J Computer Vision and Pattern Recognition Workshops (CVPRW)
%T Higher Order Multiple Object Tracking for Crowded Scenes
%U https://omnomnom.vision.rwth-aachen.de/data/RobMOTS/workshop/papers/9/CameraReady/paper_V3.pdf - Hinrichs, R., Dunkel, J., and Ostermann, J. (2021)Mixing Time-Frequency Distributions for Speech Command Recognition using Convolutional Neural Networks. In 6th International Conference on Frontiers of Signal Processing (ICFSP 2021).
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author = {Hinrichs, Reemt and Dunkel, Jonas and Ostermann, J{ö}rn},
booktitle = {6th International Conference on Frontiers of Signal Processing (ICFSP 2021)},
keywords = {Recognition},
month = {09},
title = {Mixing Time-Frequency Distributions for Speech Command Recognition using Convolutional Neural Networks},
year = 2021
}%0 Conference Paper
%1 HinDun2021
%A Hinrichs, Reemt
%A Dunkel, Jonas
%A Ostermann, J{ö}rn
%B 6th International Conference on Frontiers of Signal Processing (ICFSP 2021)
%D 2021
%T Mixing Time-Frequency Distributions for Speech Command Recognition using Convolutional Neural Networks - He, S., Liao, W., Yang, M. Y., Yang, Y., Song, Y.-Z., Rosenhahn, B., and Xiang, T. (2021)Context-Aware Layout to Image Generation with Enhanced Object Appearance. In IEEE Conference on Computer Vision and Pattern Recognition.
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author = {He, Sen and Liao, Wentong and Yang, Michael Ying and Yang, Yongxin and Song, Yi-Zhe and Rosenhahn, Bodo and Xiang, Tao},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
keywords = {Context-Aware},
month = {06},
title = {Context-Aware Layout to Image Generation with Enhanced Object Appearance},
year = 2021
}%0 Conference Paper
%1 HeLia2021
%A He, Sen
%A Liao, Wentong
%A Yang, Michael Ying
%A Yang, Yongxin
%A Song, Yi-Zhe
%A Rosenhahn, Bodo
%A Xiang, Tao
%B IEEE Conference on Computer Vision and Pattern Recognition
%D 2021
%T Context-Aware Layout to Image Generation with Enhanced Object Appearance - Dong, N. T., Brogden, G., Gerold, G., and Khosla, M. (2021)A multitask transfer learning framework for the prediction of virus-human protein--protein interactions, BMC Bioinformatics 22, 572.Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein--protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses.
@article{dong21,
abstract = {Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein--protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses.},
author = {Dong, Ngan Thi and Brogden, Graham and Gerold, Gisa and Khosla, Megha},
journal = {BMC Bioinformatics},
keywords = {l3s},
number = 1,
pages = 572,
title = {A multitask transfer learning framework for the prediction of virus-human protein--protein interactions},
volume = 22,
year = 2021
}%0 Journal Article
%1 dong21
%A Dong, Ngan Thi
%A Brogden, Graham
%A Gerold, Gisa
%A Khosla, Megha
%D 2021
%J BMC Bioinformatics
%N 1
%P 572
%R 10.1186/s12859-021-04484-y
%T A multitask transfer learning framework for the prediction of virus-human protein--protein interactions
%U https://doi.org/10.1186/s12859-021-04484-y
%V 22
%X Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein--protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses. - Eimer, T., Biedenkapp, A., Reimer, M., Adriaensen, S., Hutter, F., and Lindauer, M. (2021)DACBench: A Benchmark Library for Dynamic Algorithm Configuration. In Proceedings of the international joint conference on artificial intelligence (IJCAI).
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author = {Eimer, Theresa and Biedenkapp, André and Reimer, Maximilian and Adriaensen, Steven and Hutter, Frank and Lindauer, Marius},
booktitle = {Proceedings of the international joint conference on artificial intelligence (IJCAI)},
keywords = {leibnizailab},
month = {08},
note = {To appear.},
title = {DACBench: A Benchmark Library for Dynamic Algorithm Configuration},
year = 2021
}%0 Conference Paper
%1 EimBie2021b
%A Eimer, Theresa
%A Biedenkapp, André
%A Reimer, Maximilian
%A Adriaensen, Steven
%A Hutter, Frank
%A Lindauer, Marius
%B Proceedings of the international joint conference on artificial intelligence (IJCAI)
%D 2021
%T DACBench: A Benchmark Library for Dynamic Algorithm Configuration
%U https://arxiv.org/abs/2105.08541 - Hornakova*, A., Kaiser*, T., Rolinek, M., Rosenhahn, B., Swoboda, P., Henschel, R., and equal contribution), (*. (2021)Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths. In International Conference on Computer Vision (ICCV).
@inproceedings{HorKai2021,
author = {Hornakova*, Andrea and Kaiser*, Timo and Rolinek, Michal and Rosenhahn, Bodo and Swoboda, Paul and Henschel, Roberto and equal contribution), (*},
booktitle = {International Conference on Computer Vision (ICCV)},
keywords = {Scalable},
month = 10,
title = {Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths},
year = 2021
}%0 Conference Paper
%1 HorKai2021
%A Hornakova*, Andrea
%A Kaiser*, Timo
%A Rolinek, Michal
%A Rosenhahn, Bodo
%A Swoboda, Paul
%A Henschel, Roberto
%A equal contribution), (*
%B International Conference on Computer Vision (ICCV)
%D 2021
%T Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths
%U https://arxiv.org/abs/2108.10606 - Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R., and Hutter, F. (2021)SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. In ArXiv: 2109.09831.
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author = {Lindauer, Marius and Eggensperger, Katharina and Feurer, Matthias and Biedenkapp, André and Deng, Difan and Benjamins, Carolin and Sass, René and Hutter, Frank},
booktitle = {ArXiv: 2109.09831},
keywords = {SMAC3},
title = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
year = 2021
}%0 Conference Paper
%1 LinEgg2021
%A Lindauer, Marius
%A Eggensperger, Katharina
%A Feurer, Matthias
%A Biedenkapp, André
%A Deng, Difan
%A Benjamins, Carolin
%A Sass, René
%A Hutter, Frank
%B ArXiv: 2109.09831
%D 2021
%T SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
%U https://arxiv.org/abs/2109.09831 - Knura, M., Kluger, F., Zahtila, M., Schiewe, J., Rosenhahn, B., and Burghardt, D. (2021)Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden, ISPRS International Journal of Geo-Information.
@article{KnuKlu2021,
author = {Knura, Martin and Kluger, Florian and Zahtila, Moris and Schiewe, Jochen and Rosenhahn, Bodo and Burghardt, Dirk},
journal = {ISPRS International Journal of Geo-Information},
keywords = {Bicycle},
month = 10,
title = {Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden},
year = 2021
}%0 Journal Article
%1 KnuKlu2021
%A Knura, Martin
%A Kluger, Florian
%A Zahtila, Moris
%A Schiewe, Jochen
%A Rosenhahn, Bodo
%A Burghardt, Dirk
%D 2021
%J ISPRS International Journal of Geo-Information
%R 10.3390/ijgi10110733
%T Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden
%U https://doi.org/10.3390%2Fijgi10110733 - Mukherjee, R., Naik, A., Poddar, S., Dasgupta, S., and Ganguly, N. (2021)Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach. In .We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution.
@inproceedings{mukherjee2021understanding,
abstract = {We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution.},
author = {Mukherjee, Rajdeep and Naik, Atharva and Poddar, Sriyash and Dasgupta, Soham and Ganguly, Niloy},
keywords = {l3s},
note = {cite arxiv:2105.03983Comment: 5 pages, Short Paper accepted at SIGIR 2021},
title = {Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach},
year = 2021
}%0 Conference Paper
%1 mukherjee2021understanding
%A Mukherjee, Rajdeep
%A Naik, Atharva
%A Poddar, Sriyash
%A Dasgupta, Soham
%A Ganguly, Niloy
%D 2021
%R 10.1145/3404835.3463080
%T Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach
%U http://arxiv.org/abs/2105.03983
%X We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution. - Gritzner, D., and Ostermann, J. (2021)Semantic Segmentation of Aerial Images Using Binary Space Partitioning. In KI 2021: Advances in Artificial Intelligence, pp. 116–134.
@inproceedings{GriOst2021a,
author = {Gritzner, Daniel and Ostermann, J{ö}rn},
booktitle = {KI 2021: Advances in Artificial Intelligence},
keywords = {Partitioning},
pages = {116--134},
title = {Semantic Segmentation of Aerial Images Using Binary Space Partitioning},
year = 2021
}%0 Conference Paper
%1 GriOst2021a
%A Gritzner, Daniel
%A Ostermann, J{ö}rn
%B KI 2021: Advances in Artificial Intelligence
%D 2021
%P 116--134
%R 10.1007/978-3-030-87626-5_10
%T Semantic Segmentation of Aerial Images Using Binary Space Partitioning
%@ 978-3-030-87626-5 - Mukherjee, A., Mallick, M., Chakraborty, S., and Ganguly, N. (2021)Unsupervised Topology Assessment in Smart Homes. In 8th ACM IKDD CODS and 26th COMAD, pp. 193–197, Association for Computing Machinery, Bangalore, India.Nowadays, a wide range of IOT devices are deployed in a variety of environments and settings to enhance the quality of human life. With a huge amount of data being generated from them, privacy is becoming a very big concern. To determine the level of privacy breach that can be achieved, we introduce in this paper, an unsupervised approach to visualize the sensor network, which in turn divulges the indoor topology of a smart home. The results are obtained from a smart environment by conducting a series of deductions and analysis on sensor datasets generated by a smart home. The experimental results demonstrate that our approach is able to deduce room-level sensor topology for a smart home even without the knowledge of any activity label or any prior information about the environment.
@inproceedings{10.1145/3430984.3431028,
abstract = {Nowadays, a wide range of IOT devices are deployed in a variety of environments and settings to enhance the quality of human life. With a huge amount of data being generated from them, privacy is becoming a very big concern. To determine the level of privacy breach that can be achieved, we introduce in this paper, an unsupervised approach to visualize the sensor network, which in turn divulges the indoor topology of a smart home. The results are obtained from a smart environment by conducting a series of deductions and analysis on sensor datasets generated by a smart home. The experimental results demonstrate that our approach is able to deduce room-level sensor topology for a smart home even without the knowledge of any activity label or any prior information about the environment.},
address = {New York, NY, USA},
author = {Mukherjee, Avirup and Mallick, Madhumita and Chakraborty, Sandip and Ganguly, Niloy},
booktitle = {8th ACM IKDD CODS and 26th COMAD},
keywords = {leibnizailab},
pages = {193–197},
publisher = {Association for Computing Machinery},
series = {CODS COMAD 2021},
title = {Unsupervised Topology Assessment in Smart Homes},
year = 2021
}%0 Conference Paper
%1 10.1145/3430984.3431028
%A Mukherjee, Avirup
%A Mallick, Madhumita
%A Chakraborty, Sandip
%A Ganguly, Niloy
%B 8th ACM IKDD CODS and 26th COMAD
%C New York, NY, USA
%D 2021
%I Association for Computing Machinery
%P 193–197
%R 10.1145/3430984.3431028
%T Unsupervised Topology Assessment in Smart Homes
%U https://doi.org/10.1145/3430984.3431028
%X Nowadays, a wide range of IOT devices are deployed in a variety of environments and settings to enhance the quality of human life. With a huge amount of data being generated from them, privacy is becoming a very big concern. To determine the level of privacy breach that can be achieved, we introduce in this paper, an unsupervised approach to visualize the sensor network, which in turn divulges the indoor topology of a smart home. The results are obtained from a smart environment by conducting a series of deductions and analysis on sensor datasets generated by a smart home. The experimental results demonstrate that our approach is able to deduce room-level sensor topology for a smart home even without the knowledge of any activity label or any prior information about the environment.
%@ 9781450388177 - Wehrbein, T., Rudolph, M., Rosenhahn, B., and Wandt, B. (2021)Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows. In International Conference on Computer Vision (ICCV).
@inproceedings{WehRud2021a,
author = {Wehrbein, Tom and Rudolph, Marco and Rosenhahn, Bodo and Wandt, Bastian},
booktitle = {International Conference on Computer Vision (ICCV)},
keywords = {Monocular},
month = 10,
title = {Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows},
year = 2021
}%0 Conference Paper
%1 WehRud2021a
%A Wehrbein, Tom
%A Rudolph, Marco
%A Rosenhahn, Bodo
%A Wandt, Bastian
%B International Conference on Computer Vision (ICCV)
%D 2021
%T Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows
%U /brokenurl#arxiv, code - Chin, T.-J., Suter, D., Ch’ng, S.-F., and Quach, J. (2021)Quantum Robust Fitting. In Computer Vision -- ACCV 2020 (Ishikawa, H., Liu, C.-L., Pajdla, T., and Shi, J., Eds.), pp. 485–499, Springer International Publishing, Cham.Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by the analysis of monotone Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision.
@inproceedings{10.1007/978-3-030-69525-5_29,
abstract = {Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by the analysis of monotone Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision.},
address = {Cham},
author = {Chin, Tat-Jun and Suter, David and Ch'ng, Shin-Fang and Quach, James},
booktitle = {Computer Vision -- ACCV 2020},
editor = {Ishikawa, Hiroshi and Liu, Cheng-Lin and Pajdla, Tomas and Shi, Jianbo},
keywords = {leibnizailab},
pages = {485--499},
publisher = {Springer International Publishing},
title = {Quantum Robust Fitting},
year = 2021
}%0 Conference Paper
%1 10.1007/978-3-030-69525-5_29
%A Chin, Tat-Jun
%A Suter, David
%A Ch'ng, Shin-Fang
%A Quach, James
%B Computer Vision -- ACCV 2020
%C Cham
%D 2021
%E Ishikawa, Hiroshi
%E Liu, Cheng-Lin
%E Pajdla, Tomas
%E Shi, Jianbo
%I Springer International Publishing
%P 485--499
%R 10.1007/978-3-030-69525-5_29
%T Quantum Robust Fitting
%X Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by the analysis of monotone Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision.
%@ 978-3-030-69525-5 - Kellermann, C., Neumann, E., and Ostermann, J. (2021)A New Preprocessing Approach to Reduce Computational Complexity for Time Series Forecasting with Neuronal Networks: Temporal Resolution Warping. In 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), pp. 324–328.
@inproceedings{KelNeu2021,
author = {Kellermann, Christoph and Neumann, Erik and Ostermann, J{ö}rn},
booktitle = {2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)},
keywords = {Reduce},
pages = {324-328},
title = {A New Preprocessing Approach to Reduce Computational Complexity for Time Series Forecasting with Neuronal Networks: Temporal Resolution Warping},
year = 2021
}%0 Conference Paper
%1 KelNeu2021
%A Kellermann, Christoph
%A Neumann, Erik
%A Ostermann, J{ö}rn
%B 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)
%D 2021
%P 324-328
%R 10.1109/ISCSIC54682.2021.00065
%T A New Preprocessing Approach to Reduce Computational Complexity for Time Series Forecasting with Neuronal Networks: Temporal Resolution Warping - Kellermann, C., and Ostermann, J. (2021)Estimation of unknown system states based on an adaptive neural network and Kalman filter, Procedia CIRP 99, 656–661.
@article{KelOst2021,
author = {Kellermann, Christoph and Ostermann, J{ö}rn},
journal = {Procedia CIRP},
keywords = {unknown},
month = {07},
note = {14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 15-17 July 2020},
number = 14,
pages = {656-661},
title = {Estimation of unknown system states based on an adaptive neural network and Kalman filter},
volume = 99,
year = 2021
}%0 Journal Article
%1 KelOst2021
%A Kellermann, Christoph
%A Ostermann, J{ö}rn
%D 2021
%J Procedia CIRP
%N 14
%P 656-661
%R https://doi.org/10.1016/j.procir.2021.03.089
%T Estimation of unknown system states based on an adaptive neural network and Kalman filter
%V 99 - Olatunji, I. E., Nejdl, W., and Khosla, M. (2021)Membership inference attack on graph neural networks. In IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (short version presented in ICLR-21 Workshop on Distributed and Private Machine Learning (DPML) ).
@inproceedings{olatunji2021membership,
author = {Olatunji, Iyiola E and Nejdl, Wolfgang and Khosla, Megha},
booktitle = {IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (short version presented in ICLR-21 Workshop on Distributed and Private Machine Learning (DPML) )},
keywords = {l3s},
title = {Membership inference attack on graph neural networks},
year = 2021
}%0 Conference Paper
%1 olatunji2021membership
%A Olatunji, Iyiola E
%A Nejdl, Wolfgang
%A Khosla, Megha
%B IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (short version presented in ICLR-21 Workshop on Distributed and Private Machine Learning (DPML) )
%D 2021
%T Membership inference attack on graph neural networks - Dockhorn, A., Hurtado-Grueso, J., Jeurissen, D., Xu, L., and Perez-Liebana, D. (2021)Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing. In Proceedings of the 2021 IEEE Conference on Games (CoG), pp. 1–8.
@inproceedings{DocHur2021a,
author = {Dockhorn, Alexander and Hurtado-Grueso, Jorge and Jeurissen, Dominik and Xu, Linjie and Perez-Liebana, Diego},
booktitle = {Proceedings of the 2021 IEEE Conference on Games (CoG)},
keywords = {Monte},
month = {08},
pages = {1--8},
title = {Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing},
year = 2021
}%0 Conference Paper
%1 DocHur2021a
%A Dockhorn, Alexander
%A Hurtado-Grueso, Jorge
%A Jeurissen, Dominik
%A Xu, Linjie
%A Perez-Liebana, Diego
%B Proceedings of the 2021 IEEE Conference on Games (CoG)
%D 2021
%P 1--8
%R 10.1109/CoG52621.2021.9619029
%T Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing
%U https://ieeexplore.ieee.org/document/9619029 - Liao, W., Lan, C., Yang, M. Y., Zeng, W., and Rosenhahn, B. (2021)Target-Tailored Source-Transformation for Scene Graph Generation. In In CVPR Workshop on Multi-Sensor Fusion for Dynamic Scene Understanding.
@inproceedings{LiaLan2021,
author = {Liao, Wentong and Lan, Cuiling and Yang, Michael Ying and Zeng, Wenjung and Rosenhahn, Bodo},
booktitle = {In CVPR Workshop on Multi-Sensor Fusion for Dynamic Scene Understanding},
keywords = {Target-Tailored},
month = {06},
title = {Target-Tailored Source-Transformation for Scene Graph Generation},
year = 2021
}%0 Conference Paper
%1 LiaLan2021
%A Liao, Wentong
%A Lan, Cuiling
%A Yang, Michael Ying
%A Zeng, Wenjung
%A Rosenhahn, Bodo
%B In CVPR Workshop on Multi-Sensor Fusion for Dynamic Scene Understanding
%D 2021
%T Target-Tailored Source-Transformation for Scene Graph Generation - Schubert, F., Eimer, T., Rosenhahn, B., and Lindauer, M. (2021)Automatic Risk Adaptation in Distributional Reinforcement Learning. In Arxiv Preprint.
@inproceedings{SchEim2021,
author = {Schubert, Frederik and Eimer, Theresa and Rosenhahn, Bodo and Lindauer, Marius},
booktitle = {Arxiv Preprint},
keywords = {Adaptation},
month = {06},
title = {Automatic Risk Adaptation in Distributional Reinforcement Learning},
year = 2021
}%0 Conference Paper
%1 SchEim2021
%A Schubert, Frederik
%A Eimer, Theresa
%A Rosenhahn, Bodo
%A Lindauer, Marius
%B Arxiv Preprint
%D 2021
%T Automatic Risk Adaptation in Distributional Reinforcement Learning
%U https://arxiv.org/abs/2106.06317 - Gritzner, D., and Ostermann, J. (2021)Minimizing Manual Labeling Effort for The Semantic Segmentation of Aerial Images. In 2021 IEEE Statistical Signal Processing Workshop (SSP), pp. 81–85.
@inproceedings{GriOst2021,
author = {Gritzner, Daniel and Ostermann, J{ö}rn},
booktitle = {2021 IEEE Statistical Signal Processing Workshop (SSP)},
keywords = {Aerial},
pages = {81--85},
title = {Minimizing Manual Labeling Effort for The Semantic Segmentation of Aerial Images},
year = 2021
}%0 Conference Paper
%1 GriOst2021
%A Gritzner, Daniel
%A Ostermann, J{ö}rn
%B 2021 IEEE Statistical Signal Processing Workshop (SSP)
%D 2021
%P 81--85
%T Minimizing Manual Labeling Effort for The Semantic Segmentation of Aerial Images - Kuhnke, F., Ihler, S., and Ostermann, J. (2021)Relative Pose Consistency for Semi-Supervised Head Pose Estimation. In 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021).
@inproceedings{KuhIhl2021,
author = {Kuhnke, Felix and Ihler, Sontje and Ostermann, J{ö}rn},
booktitle = {16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)},
keywords = {Head},
month = 12,
title = {Relative Pose Consistency for Semi-Supervised Head Pose Estimation},
year = 2021
}%0 Conference Paper
%1 KuhIhl2021
%A Kuhnke, Felix
%A Ihler, Sontje
%A Ostermann, J{ö}rn
%B 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
%D 2021
%T Relative Pose Consistency for Semi-Supervised Head Pose Estimation - Dockhorn, A., Mostaghim, S., Kirst, M., and Zettwitz, M. (2021)Multi-Objective Optimization and Decision-Making in Context Steering. In 2021 IEEE Conference on Games (CoG), pp. 1–8.
@inproceedings{DocMos2021a,
author = {Dockhorn, Alexander and Mostaghim, Sanaz and Kirst, Martin and Zettwitz, Martin},
booktitle = {2021 IEEE Conference on Games (CoG)},
keywords = {Optimization},
pages = {1-8},
title = {Multi-Objective Optimization and Decision-Making in Context Steering},
year = 2021
}%0 Conference Paper
%1 DocMos2021a
%A Dockhorn, Alexander
%A Mostaghim, Sanaz
%A Kirst, Martin
%A Zettwitz, Martin
%B 2021 IEEE Conference on Games (CoG)
%D 2021
%P 1-8
%R 10.1109/CoG52621.2021.9619155
%T Multi-Objective Optimization and Decision-Making in Context Steering
%U https://ieeexplore.ieee.org/document/9619155
%@ 978-1-6654-3886-5 - Nandy, A., Sharma, S., Maddhashiya, S., Sachdeva, K., Goyal, P., and Ganguly, N. (2021)Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework, pp. 4600–4609, Association for Computational Linguistics.Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.
@proceedings{nandy2021question,
abstract = {Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.},
address = {Association for Computational Linguistics},
author = {Nandy, Abhilash and Sharma, Soumya and Maddhashiya, Shubham and Sachdeva, Kapil and Goyal, Pawan and Ganguly, NIloy},
howpublished = {Punta Cana, Dominican Republic},
keywords = {leibnizailab},
pages = {4600-4609},
title = {Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework},
year = 2021
}%0 Conference Proceedings
%1 nandy2021question
%A Nandy, Abhilash
%A Sharma, Soumya
%A Maddhashiya, Shubham
%A Sachdeva, Kapil
%A Goyal, Pawan
%A Ganguly, NIloy
%C Association for Computational Linguistics
%D 2021
%P 4600-4609
%T Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework
%U https://aclanthology.org/2021.findings-emnlp.392
%X Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home. - Dockhorn, A., and Kruse, R. (2021)Fuzzy Modeling in Game AI, Journal of Pure and Applied Mathematics 12, 54–68.
@article{DocKru2021,
author = {Dockhorn, Alexander and Kruse, Rudolf},
journal = {Journal of Pure and Applied Mathematics},
keywords = {Fuzzy},
number = 1,
pages = {54-68},
title = {Fuzzy Modeling in Game AI},
volume = 12,
year = 2021
}%0 Journal Article
%1 DocKru2021
%A Dockhorn, Alexander
%A Kruse, Rudolf
%D 2021
%J Journal of Pure and Applied Mathematics
%N 1
%P 54-68
%T Fuzzy Modeling in Game AI
%U http://www.twmsj.az/Files/Contents%20V.12%20N.1.2021/pp54-68.pdf
%V 12 - Adhisantoso, Y. G. (2021)Cross-check CE3 Extension of Contact Matrix Compressor m58074, ISO/IEC JTC 1/SC 29/WG 8.
@article{Adh2021c,
author = {Adhisantoso, Yeremia Gunawan},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {m58074},
month = 10,
title = {Cross-check CE3 Extension of Contact Matrix Compressor m58074},
year = 2021
}%0 Journal Article
%1 Adh2021c
%A Adhisantoso, Yeremia Gunawan
%D 2021
%J ISO/IEC JTC 1/SC 29/WG 8
%T Cross-check CE3 Extension of Contact Matrix Compressor m58074 - Luo, C., Lin, J., Cai, S., Chen, X., He, B., Qiao, B., Zhao, P., Lin, Q., Zhang, H., Wu, W., Rajmohan, S., and Zhang, D. (2021)AutoCCAG: An Automated Approach to Constrained Covering Array Generation. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), pp. 201–212.Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t>=4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems.
@inproceedings{9402109,
abstract = {Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t>=4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems.},
author = {Luo, Chuan and Lin, Jinkun and Cai, Shaowei and Chen, Xin and He, Bing and Qiao, Bo and Zhao, Pu and Lin, Qingwei and Zhang, Hongyu and Wu, Wei and Rajmohan, Saravanakumar and Zhang, Dongmei},
booktitle = {2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)},
keywords = {l3s},
month = {05},
pages = {201-212},
title = {AutoCCAG: An Automated Approach to Constrained Covering Array Generation},
year = 2021
}%0 Conference Paper
%1 9402109
%A Luo, Chuan
%A Lin, Jinkun
%A Cai, Shaowei
%A Chen, Xin
%A He, Bing
%A Qiao, Bo
%A Zhao, Pu
%A Lin, Qingwei
%A Zhang, Hongyu
%A Wu, Wei
%A Rajmohan, Saravanakumar
%A Zhang, Dongmei
%B 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)
%D 2021
%P 201-212
%R 10.1109/ICSE43902.2021.00030
%T AutoCCAG: An Automated Approach to Constrained Covering Array Generation
%U https://ieeexplore.ieee.org/document/9402109
%X Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t>=4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems. - Luo, C., Zhao, P., Qiao, B., Wu, Y., Zhang, H., Wu, W., Lu, W., Dang, Y., Rajmohan, S., Lin, Q., and Zhang, D. (2021)NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms. In Proceedings of the Web Conference 2021, {ACM}.
@inproceedings{Luo_2021,
author = {Luo, Chuan and Zhao, Pu and Qiao, Bo and Wu, Youjiang and Zhang, Hongyu and Wu, Wei and Lu, Weihai and Dang, Yingnong and Rajmohan, Saravanakumar and Lin, Qingwei and Zhang, Dongmei},
booktitle = {Proceedings of the Web Conference 2021},
keywords = {cloudplatforms},
month = {04},
publisher = {{ACM}},
title = {NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms},
year = 2021
}%0 Conference Paper
%1 Luo_2021
%A Luo, Chuan
%A Zhao, Pu
%A Qiao, Bo
%A Wu, Youjiang
%A Zhang, Hongyu
%A Wu, Wei
%A Lu, Weihai
%A Dang, Yingnong
%A Rajmohan, Saravanakumar
%A Lin, Qingwei
%A Zhang, Dongmei
%B Proceedings of the Web Conference 2021
%D 2021
%I {ACM}
%R 10.1145/3442381.3449867
%T NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms
%U https://doi.org/10.1145%2F3442381.3449867 - Liu, Z., Pavao, A., Xu, Z., Escalera, S., Ferreira, F., Gyon, I., Hong, S., Hutter, F., Ji, R., Junior, J. J., Li, G., Lindauer, M., Luo, Z., Madadi, M., Nierhoff, T., Niu, K., Pan, C., Stoll, D., Treguer, S., Jin, W., Wang, P., Wu, C., Youcheng, X., Zela, A., and Zhang, Y. (2021)Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence 1–18.
@article{LiuPav2021a,
author = {Liu, Zhengying and Pavao, Adrien and Xu, Zhen and Escalera, Sergio and Ferreira, Fabio and Gyon, Isabelle and Hong, Sirui and Hutter, Frank and Ji, Rongrong and Junior, Julio Jacques and Li, Ge and Lindauer, Marius and Luo, Zhipeng and Madadi, Meysam and Nierhoff, Thomas and Niu, Kangning and Pan, Chunguang and Stoll, Danny and Treguer, Sebastien and Jin, Wang and Wang, Peng and Wu, Chenglin and Youcheng, Xiong and Zela, Arber and Zhang, Yang},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {ChaLearn},
note = {To appear},
pages = {1-18},
title = {Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019},
year = 2021
}%0 Journal Article
%1 LiuPav2021a
%A Liu, Zhengying
%A Pavao, Adrien
%A Xu, Zhen
%A Escalera, Sergio
%A Ferreira, Fabio
%A Gyon, Isabelle
%A Hong, Sirui
%A Hutter, Frank
%A Ji, Rongrong
%A Junior, Julio Jacques
%A Li, Ge
%A Lindauer, Marius
%A Luo, Zhipeng
%A Madadi, Meysam
%A Nierhoff, Thomas
%A Niu, Kangning
%A Pan, Chunguang
%A Stoll, Danny
%A Treguer, Sebastien
%A Jin, Wang
%A Wang, Peng
%A Wu, Chenglin
%A Youcheng, Xiong
%A Zela, Arber
%A Zhang, Yang
%D 2021
%J IEEE Transactions on Pattern Analysis and Machine Intelligence
%P 1-18
%T Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019 - Nandy, A., Sharma, S., Maddhashiya, S., Sachdeva, K., Goyal, P., and Ganguly, N. (2021)Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework. In Findings of the Association for Computational Linguistics: {EMNLP} 2021, Association for Computational Linguistics.
@inproceedings{Nandy_2021,
author = {Nandy, Abhilash and Sharma, Soumya and Maddhashiya, Shubham and Sachdeva, Kapil and Goyal, Pawan and Ganguly, NIloy},
booktitle = {Findings of the Association for Computational Linguistics: {EMNLP} 2021},
keywords = {QA},
publisher = {Association for Computational Linguistics},
title = {Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework},
year = 2021
}%0 Conference Paper
%1 Nandy_2021
%A Nandy, Abhilash
%A Sharma, Soumya
%A Maddhashiya, Shubham
%A Sachdeva, Kapil
%A Goyal, Pawan
%A Ganguly, NIloy
%B Findings of the Association for Computational Linguistics: {EMNLP} 2021
%D 2021
%I Association for Computational Linguistics
%R 10.18653/v1/2021.findings-emnlp.392
%T Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework
%U https://doi.org/10.18653%2Fv1%2F2021.findings-emnlp.392 - Samanta, B., Agrawal, M., and Ganguly, N. (2021)A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow, pp. 2405–2415, Association for Computational Linguistics.In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.
@proceedings{samanta2021hierarchical,
abstract = {In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.},
address = {Association for Computational Linguistics},
author = {Samanta, Bidisha and Agrawal, Mohit and Ganguly, NIloy},
howpublished = {Online},
keywords = {leibnizailab},
pages = {2405-2415},
title = {A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow},
year = 2021
}%0 Conference Proceedings
%1 samanta2021hierarchical
%A Samanta, Bidisha
%A Agrawal, Mohit
%A Ganguly, NIloy
%C Association for Computational Linguistics
%D 2021
%P 2405-2415
%T A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow
%U https://aclanthology.org/2021.acl-long.187
%X In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute. - Das, S., Patibandla, H., Bhattacharya, S., Bera, K., Ganguly, N., and Bhattacharya, S. (2021)TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving. In ICCV.
@inproceedings{das2021tmcoss,
author = {Das, Soumi and Patibandla, Harikrishna and Bhattacharya, Suparna and Bera, Kshounis and Ganguly, Niloy and Bhattacharya, Sourangshu},
booktitle = {ICCV},
keywords = {leibnizailab},
title = {TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving},
year = 2021
}%0 Conference Paper
%1 das2021tmcoss
%A Das, Soumi
%A Patibandla, Harikrishna
%A Bhattacharya, Suparna
%A Bera, Kshounis
%A Ganguly, Niloy
%A Bhattacharya, Sourangshu
%B ICCV
%D 2021
%T TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving - Mukherjee, R., Naik, A., Poddar, S., Dasgupta, S., and Ganguly, N. (2021)Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach. In SIGIR 2021.We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution.
@inproceedings{mukherjee2021understanding,
abstract = {We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution.},
author = {Mukherjee, Rajdeep and Naik, Atharva and Poddar, Sriyash and Dasgupta, Soham and Ganguly, Niloy},
booktitle = {SIGIR 2021},
keywords = {leibnizailab},
title = {Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach},
year = 2021
}%0 Conference Paper
%1 mukherjee2021understanding
%A Mukherjee, Rajdeep
%A Naik, Atharva
%A Poddar, Sriyash
%A Dasgupta, Soham
%A Ganguly, Niloy
%B SIGIR 2021
%D 2021
%R 10.1145/3404835.3463080
%T Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach
%U http://arxiv.org/abs/2105.03983
%X We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution. - Adhisantoso, Y. G., and Ostermann, J. (2021)Method for the Coding of Contact Matrix m56622, ISO/IEC JTC 1/SC 29/WG 8.
@article{AdhO2021,
author = {Adhisantoso, Yeremia Gunawan and Ostermann, J{ö}rn},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {Method},
month = {04},
title = {Method for the Coding of Contact Matrix m56622},
year = 2021
}%0 Journal Article
%1 AdhO2021
%A Adhisantoso, Yeremia Gunawan
%A Ostermann, J{ö}rn
%D 2021
%J ISO/IEC JTC 1/SC 29/WG 8
%T Method for the Coding of Contact Matrix m56622 - Rudolph, M., Wandt, B., and Rosenhahn, B. (2021)Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows. In Winter Conference on Applications of Computer Vision (WACV).
@inproceedings{RudWan2021a,
author = {Rudolph, Marco and Wandt, Bastian and Rosenhahn, Bodo},
booktitle = {Winter Conference on Applications of Computer Vision (WACV)},
keywords = {Detection},
month = {01},
title = {Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows},
year = 2021
}%0 Conference Paper
%1 RudWan2021a
%A Rudolph, Marco
%A Wandt, Bastian
%A Rosenhahn, Bodo
%B Winter Conference on Applications of Computer Vision (WACV)
%D 2021
%T Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
%U /brokenurl#arxiv, GitHub, YouTube - Voges, J., Hernaez, M., Mattavelli, M., and Ostermann, J. (2021)An Introduction to MPEG-G: The First Open ISO/IEC Standard for the Compression and Exchange of Genomic Sequencing Data, Proceedings of the IEEE 109, 1607–1622.
@article{VogHer2021a,
author = {Voges, Jan and Hernaez, Mikel and Mattavelli, Marco and Ostermann, J{ö}rn},
journal = {Proceedings of the IEEE},
keywords = {to},
number = 9,
pages = {1607-1622},
title = {An Introduction to MPEG-G: The First Open ISO/IEC Standard for the Compression and Exchange of Genomic Sequencing Data},
volume = 109,
year = 2021
}%0 Journal Article
%1 VogHer2021a
%A Voges, Jan
%A Hernaez, Mikel
%A Mattavelli, Marco
%A Ostermann, J{ö}rn
%D 2021
%J Proceedings of the IEEE
%N 9
%P 1607-1622
%R 10.1109/JPROC.2021.3082027
%T An Introduction to MPEG-G: The First Open ISO/IEC Standard for the Compression and Exchange of Genomic Sequencing Data
%U https://doi.org/10.1109/JPROC.2021.3082027
%V 109 - Hartmann, F., and Ostermann, J. (2021)Investigation of the Effect of the Flight Path on the Three Dimensional Locatability of Targets. In Synthetic Aperture Radar (APSAR), 2021 IEEE 7th Asia-Pacific Conference.
@inproceedings{HarOst2021,
author = {Hartmann, Fabian and Ostermann, J{ö}rn},
booktitle = {Synthetic Aperture Radar (APSAR), 2021 IEEE 7th Asia-Pacific Conference},
keywords = {Effect},
month = 11,
title = {Investigation of the Effect of the Flight Path on the Three Dimensional Locatability of Targets},
year = 2021
}%0 Conference Paper
%1 HarOst2021
%A Hartmann, Fabian
%A Ostermann, J{ö}rn
%B Synthetic Aperture Radar (APSAR), 2021 IEEE 7th Asia-Pacific Conference
%D 2021
%T Investigation of the Effect of the Flight Path on the Three Dimensional Locatability of Targets - Hachmann, H., Krüger, B., Rosenhahn, B., and Nogueira, W. (2021)Localization Of Cochlear Implant Electrodes From Cone Beam Computed Tomography Using Particle Belief Propagation. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 593–597.Cochlear implants (CIs) are implantable medical devices that can restore the hearing sense of people suffering from profound hearing loss. The CI uses a set of electrode contacts placed inside the cochlea to stimulate the auditory nerve with current pulses. The exact location of these electrodes may be an important parameter to improve and predict the performance with these devices. Currently the methods used in clinics to characterize the geometry of the cochlea as well as to estimate the electrode positions are manual, error-prone and time consuming.We propose a Markov random field (MRF) model for CI electrode localization for cone beam computed tomography (CBCT) data-sets. Intensity and shape of electrodes are included as prior knowledge as well as distance and angles between contacts. MRF inference is based on slice sampling particle belief propagation and guided by several heuristics. A stochastic search finds the best maximum a posteriori estimation among sampled MRF realizations.We evaluate our algorithm on synthetic and real CBCT data-sets and compare its performance with two state of the art algorithms. An increase of localization precision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCT data-sets is shown.
@inproceedings{9433845,
abstract = {Cochlear implants (CIs) are implantable medical devices that can restore the hearing sense of people suffering from profound hearing loss. The CI uses a set of electrode contacts placed inside the cochlea to stimulate the auditory nerve with current pulses. The exact location of these electrodes may be an important parameter to improve and predict the performance with these devices. Currently the methods used in clinics to characterize the geometry of the cochlea as well as to estimate the electrode positions are manual, error-prone and time consuming.We propose a Markov random field (MRF) model for CI electrode localization for cone beam computed tomography (CBCT) data-sets. Intensity and shape of electrodes are included as prior knowledge as well as distance and angles between contacts. MRF inference is based on slice sampling particle belief propagation and guided by several heuristics. A stochastic search finds the best maximum a posteriori estimation among sampled MRF realizations.We evaluate our algorithm on synthetic and real CBCT data-sets and compare its performance with two state of the art algorithms. An increase of localization precision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCT data-sets is shown.},
author = {Hachmann, Hendrik and Krüger, Benjamin and Rosenhahn, Bodo and Nogueira, Waldo},
booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
keywords = {l3s},
month = {04},
pages = {593-597},
title = {Localization Of Cochlear Implant Electrodes From Cone Beam Computed Tomography Using Particle Belief Propagation},
year = 2021
}%0 Conference Paper
%1 9433845
%A Hachmann, Hendrik
%A Krüger, Benjamin
%A Rosenhahn, Bodo
%A Nogueira, Waldo
%B 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
%D 2021
%P 593-597
%R 10.1109/ISBI48211.2021.9433845
%T Localization Of Cochlear Implant Electrodes From Cone Beam Computed Tomography Using Particle Belief Propagation
%U https://ieeexplore.ieee.org/abstract/document/9433845
%X Cochlear implants (CIs) are implantable medical devices that can restore the hearing sense of people suffering from profound hearing loss. The CI uses a set of electrode contacts placed inside the cochlea to stimulate the auditory nerve with current pulses. The exact location of these electrodes may be an important parameter to improve and predict the performance with these devices. Currently the methods used in clinics to characterize the geometry of the cochlea as well as to estimate the electrode positions are manual, error-prone and time consuming.We propose a Markov random field (MRF) model for CI electrode localization for cone beam computed tomography (CBCT) data-sets. Intensity and shape of electrodes are included as prior knowledge as well as distance and angles between contacts. MRF inference is based on slice sampling particle belief propagation and guided by several heuristics. A stochastic search finds the best maximum a posteriori estimation among sampled MRF realizations.We evaluate our algorithm on synthetic and real CBCT data-sets and compare its performance with two state of the art algorithms. An increase of localization precision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCT data-sets is shown. - Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., and Bischl, B. (2021)Towards Explaining Hyperparameter Optimization via Partial Dependence Plots. In Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML’21.
@inproceedings{MooHer2021,
author = {Moosbauer, Julia and Herbinger, Julia and Casalicchio, Giuseppe and Lindauer, Marius and Bischl, Bernd},
booktitle = {Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21},
keywords = {Optimization},
month = {07},
title = {Towards Explaining Hyperparameter Optimization via Partial Dependence Plots},
year = 2021
}%0 Conference Paper
%1 MooHer2021
%A Moosbauer, Julia
%A Herbinger, Julia
%A Casalicchio, Giuseppe
%A Lindauer, Marius
%A Bischl, Bernd
%B Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21
%D 2021
%T Towards Explaining Hyperparameter Optimization via Partial Dependence Plots
%U https://openreview.net/forum?id=lZr9s1x0mE - Awiszus, M., Schubert, F., and Rosenhahn, B. (2021)World-GAN: a Generative Model for Minecraft Worlds. In IEEE Conference on Games.
@inproceedings{AwiSch2021a,
author = {Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
booktitle = {IEEE Conference on Games},
keywords = {World-GAN},
month = {08},
title = {World-GAN: a Generative Model for Minecraft Worlds},
year = 2021
}%0 Conference Paper
%1 AwiSch2021a
%A Awiszus, Maren
%A Schubert, Frederik
%A Rosenhahn, Bodo
%B IEEE Conference on Games
%D 2021
%T World-GAN: a Generative Model for Minecraft Worlds
%U /brokenurl#arxiv - Kadra, A., Lindauer, M., Hutter, F., and Grabocka, J. (2021)Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS).
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author = {Kadra, Arlind and Lindauer, Marius and Hutter, Frank and Grabocka, Josif},
booktitle = {Proceedings of the international conference on Neural Information Processing Systems (NeurIPS)},
keywords = {Neural},
month = 12,
title = {Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data},
year = 2021
}%0 Conference Paper
%1 KadLin2021a
%A Kadra, Arlind
%A Lindauer, Marius
%A Hutter, Frank
%A Grabocka, Josif
%B Proceedings of the international conference on Neural Information Processing Systems (NeurIPS)
%D 2021
%T Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data
%U https://arxiv.org/abs/2106.11189 - Eimer, T., Benjamins, C., and Lindauer, M. (2021)Hyperparameters in Contextual RL are Highly Situational. In NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning.
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author = {Eimer, Theresa and Benjamins, Carolin and Lindauer, Marius},
booktitle = {NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning},
keywords = {RL},
month = 12,
title = {Hyperparameters in Contextual RL are Highly Situational},
year = 2021
}%0 Conference Paper
%1 EimBen2021a
%A Eimer, Theresa
%A Benjamins, Carolin
%A Lindauer, Marius
%B NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning
%D 2021
%T Hyperparameters in Contextual RL are Highly Situational - Eimer, T., Biedenkapp, A., Hutter, F., and Lindauer, M. (2021)Self-Paced Context Evaluation for Contextual Reinforcement Learning. In Proceedings of the international conference on machine learning (ICML).
@inproceedings{EimBie2021a,
author = {Eimer, Theresa and Biedenkapp, Andre and Hutter, Frank and Lindauer, Marius},
booktitle = {Proceedings of the international conference on machine learning (ICML)},
keywords = {Reinforcement},
month = {07},
note = {To appear},
title = {Self-Paced Context Evaluation for Contextual Reinforcement Learning},
year = 2021
}%0 Conference Paper
%1 EimBie2021a
%A Eimer, Theresa
%A Biedenkapp, Andre
%A Hutter, Frank
%A Lindauer, Marius
%B Proceedings of the international conference on machine learning (ICML)
%D 2021
%T Self-Paced Context Evaluation for Contextual Reinforcement Learning
%U https://arxiv.org/abs/2106.05110 - Biedenkapp, A., Rajan, R., Hutter, F., and Lindauer, M. (2021)TempoRL: Learning When to Act. In Proceedings of the international conference on machine learning (ICML).
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author = {Biedenkapp, Andre and Rajan, Raghu and Hutter, Frank and Lindauer, Marius},
booktitle = {Proceedings of the international conference on machine learning (ICML)},
keywords = {TempoRL},
month = {07},
note = {To appear},
title = {TempoRL: Learning When to Act},
year = 2021
}%0 Conference Paper
%1 BieRaj2021a
%A Biedenkapp, Andre
%A Rajan, Raghu
%A Hutter, Frank
%A Lindauer, Marius
%B Proceedings of the international conference on machine learning (ICML)
%D 2021
%T TempoRL: Learning When to Act
%U https://arxiv.org/abs/2106.05262 - Hartmann, F., Sommer, A., Pestel-Schiller, U., and Osterman, J. (2021)A scheme for stabilizing the image generation for VideoSAR. In 13th European Conference on Synthetic Aperture Radar.
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author = {Hartmann, Fabian and Sommer, Aron and Pestel-Schiller, Ulrike and Osterman, J{ö}rn},
booktitle = {13th European Conference on Synthetic Aperture Radar},
keywords = {leibnizailab},
month = {03},
title = {A scheme for stabilizing the image generation for VideoSAR},
year = 2021
}%0 Conference Paper
%1 HarSom2021a
%A Hartmann, Fabian
%A Sommer, Aron
%A Pestel-Schiller, Ulrike
%A Osterman, J{ö}rn
%B 13th European Conference on Synthetic Aperture Radar
%D 2021
%T A scheme for stabilizing the image generation for VideoSAR - Zimmer, L., Lindauer, M., and Hutter, F. (2021)Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL, IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 3079–3090.
@article{ZimLin2021a,
author = {Zimmer, Lucas and Lindauer, Marius and Hutter, Frank},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {MetaLearning},
month = {08},
number = 9,
pages = {3079 - 3090},
title = {Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL},
volume = 43,
year = 2021
}%0 Journal Article
%1 ZimLin2021a
%A Zimmer, Lucas
%A Lindauer, Marius
%A Hutter, Frank
%D 2021
%J IEEE Transactions on Pattern Analysis and Machine Intelligence
%N 9
%P 3079 - 3090
%T Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
%U /brokenurl#Arxiv, IEEE TPAMI
%V 43 - Kabongo, S., D’Souza, J., and Auer, S. (2021)Automated Mining of Leaderboards for Empirical {AI} Research, springer, International Conference on Asian Digital Libraries ICADL 2021: Towards Open and Trustworthy Digital Societies, 453–470.With the rapid growth of research publications, empowering scientists to keep an oversight over scientific progress is of paramount importance. In this regard, the leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressing the same research challenge. Crowdsourcing efforts like PAPERSWITHCODE among others are devoted to the construction of leaderboards predominantly for various subdomains in Artificial Intelligence. Leaderboards provide machine-readable scholarly knowledge that has proven to be directly useful for scientists to keep track of research progress – their construction could be greatly expedited with automated text mining. This study presents a comprehensive approach for generating leaderboards for knowledge-graph-based scholarly information organization. Specifically, we investigate the problem of automated leaderboard construction using state-of-the-art transformer models, viz. Bert, SciBert, and XLNet. Our analysis reveals an optimal approach that significantly outperforms existing baselines for the task with evaluation scores above 90% in F1. This, in turn, offers new state-of-the-art results for leaderboard extraction. As a result, a vast share of empirical AI research can be organized in the next-generation digital libraries as knowledge graphs.
@article{DBLP:conf/icadl/KabongoDA21,
abstract = {With the rapid growth of research publications, empowering scientists to keep an oversight over scientific progress is of paramount importance. In this regard, the leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressing the same research challenge. Crowdsourcing efforts like PAPERSWITHCODE among others are devoted to the construction of leaderboards predominantly for various subdomains in Artificial Intelligence. Leaderboards provide machine-readable scholarly knowledge that has proven to be directly useful for scientists to keep track of research progress – their construction could be greatly expedited with automated text mining. This study presents a comprehensive approach for generating leaderboards for knowledge-graph-based scholarly information organization. Specifically, we investigate the problem of automated leaderboard construction using state-of-the-art transformer models, viz. Bert, SciBert, and XLNet. Our analysis reveals an optimal approach that significantly outperforms existing baselines for the task with evaluation scores above 90% in F1. This, in turn, offers new state-of-the-art results for leaderboard extraction. As a result, a vast share of empirical AI research can be organized in the next-generation digital libraries as knowledge graphs.},
author = {Kabongo, Salomon and D'Souza, Jennifer and Auer, S{{ö}}ren},
journal = {springer, International Conference on Asian Digital Libraries},
keywords = {Scholarly-text-mining},
month = 11,
pages = {453–470},
title = {Automated Mining of Leaderboards for Empirical {AI} Research},
volume = {ICADL 2021: Towards Open and Trustworthy Digital Societies},
year = 2021
}%0 Journal Article
%1 DBLP:conf/icadl/KabongoDA21
%A Kabongo, Salomon
%A D'Souza, Jennifer
%A Auer, S{{ö}}ren
%D 2021
%J springer, International Conference on Asian Digital Libraries
%P 453–470
%R https://doi.org/10.1007/978-3-030-91669-5_35
%T Automated Mining of Leaderboards for Empirical {AI} Research
%U https://link.springer.com/chapter/10.1007/978-3-030-91669-5_35
%V ICADL 2021: Towards Open and Trustworthy Digital Societies
%X With the rapid growth of research publications, empowering scientists to keep an oversight over scientific progress is of paramount importance. In this regard, the leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressing the same research challenge. Crowdsourcing efforts like PAPERSWITHCODE among others are devoted to the construction of leaderboards predominantly for various subdomains in Artificial Intelligence. Leaderboards provide machine-readable scholarly knowledge that has proven to be directly useful for scientists to keep track of research progress – their construction could be greatly expedited with automated text mining. This study presents a comprehensive approach for generating leaderboards for knowledge-graph-based scholarly information organization. Specifically, we investigate the problem of automated leaderboard construction using state-of-the-art transformer models, viz. Bert, SciBert, and XLNet. Our analysis reveals an optimal approach that significantly outperforms existing baselines for the task with evaluation scores above 90% in F1. This, in turn, offers new state-of-the-art results for leaderboard extraction. As a result, a vast share of empirical AI research can be organized in the next-generation digital libraries as knowledge graphs. - Decker, M., Lammens, T., Ferster, A., Erlacher, M., Yoshimi, A., Niemeyer, C. M., Ernst, M. P. T., Raaijmakers, M. H. G. P., Duployez, N., Flaum, A., Steinemann, D., Schlegelberger, B., Illig, T., and Ripperger, T. (2021)Functional classification of RUNX1 variants in familial platelet disorder with associated myeloid malignancies, Leukemia.
@article{display:block){.nova-c-button--color-blue.nova-c-button--theme-solid:focus-visible{-webkit-box-shadow:0,
author = {Decker, Melanie and Lammens, Tim and Ferster, Alina and Erlacher, Miriam and Yoshimi, Ayami and Niemeyer, Charlotte M. and Ernst, Martijn P. T. and Raaijmakers, Marc H. G. P. and Duployez, Nicolas and Flaum, Andreas and Steinemann, Doris and Schlegelberger, Brigitte and Illig, Thomas and Ripperger, Tim},
journal = {Leukemia},
keywords = {l3s},
month = {07},
title = {Functional classification of RUNX1 variants in familial platelet disorder with associated myeloid malignancies},
year = 2021
}%0 Journal Article
%1 display:block){.nova-c-button--color-blue.nova-c-button--theme-solid:focus-visible{-webkit-box-shadow:0
%A Decker, Melanie
%A Lammens, Tim
%A Ferster, Alina
%A Erlacher, Miriam
%A Yoshimi, Ayami
%A Niemeyer, Charlotte M.
%A Ernst, Martijn P. T.
%A Raaijmakers, Marc H. G. P.
%A Duployez, Nicolas
%A Flaum, Andreas
%A Steinemann, Doris
%A Schlegelberger, Brigitte
%A Illig, Thomas
%A Ripperger, Tim
%D 2021
%J Leukemia
%T Functional classification of RUNX1 variants in familial platelet disorder with associated myeloid malignancies - Chouvarine, P., Anti{{{\’c}}}, {\v{Z}}eljko, Lentes, J., Schröder, C., Alten, J., Brüggemann, M., de Santa Pau, E. C., Illig, T., Laguna, T., Schewe, D., Stanulla, M., Tang, M., Zimmermann, M., Schrappe, M., Schlegelberger, B., Cario, G., and Bergmann, A. K. (2021)Transcriptional and Mutational Profiling of B-Other Acute Lymphoblastic Leukemia for Improved Diagnostics, Cancers, {MDPI} {AG} 13, 5653.
@article{Chouvarine_2021,
author = {Chouvarine, Philippe and Anti{{{\'c}}}, {\v{Z}}eljko and Lentes, Jana and Schröder, Charlotte and Alten, Julia and Brüggemann, Monika and de Santa Pau, Enrique Carrillo and Illig, Thomas and Laguna, Teresa and Schewe, Denis and Stanulla, Martin and Tang, Ming and Zimmermann, Martin and Schrappe, Martin and Schlegelberger, Brigitte and Cario, Gunnar and Bergmann, Anke K.},
journal = {Cancers},
keywords = {l3s},
month = 11,
number = 22,
pages = 5653,
publisher = {{MDPI} {AG}},
title = {Transcriptional and Mutational Profiling of B-Other Acute Lymphoblastic Leukemia for Improved Diagnostics},
volume = 13,
year = 2021
}%0 Journal Article
%1 Chouvarine_2021
%A Chouvarine, Philippe
%A Anti{{{\'c}}}, {\v{Z}}eljko
%A Lentes, Jana
%A Schröder, Charlotte
%A Alten, Julia
%A Brüggemann, Monika
%A de Santa Pau, Enrique Carrillo
%A Illig, Thomas
%A Laguna, Teresa
%A Schewe, Denis
%A Stanulla, Martin
%A Tang, Ming
%A Zimmermann, Martin
%A Schrappe, Martin
%A Schlegelberger, Brigitte
%A Cario, Gunnar
%A Bergmann, Anke K.
%D 2021
%I {MDPI} {AG}
%J Cancers
%N 22
%P 5653
%R 10.3390/cancers13225653
%T Transcriptional and Mutational Profiling of B-Other Acute Lymphoblastic Leukemia for Improved Diagnostics
%U https://doi.org/10.3390%2Fcancers13225653
%V 13 - Booth, A., Reed, A. B., Ponzo, S., Yassaee, A., Aral, M., Plans, D., Labrique, A., and Mohan, D. (2021)Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis, PLOS ONE, Public Library of Science 16, 1–30.Aim COVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19. Methods The systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1st January 2020 and 9th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable. Results Seventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR: 2.65, 95% CI: 1.81–3.90), male sex (OR: 2.05, 95% CI: 1.39–3.04) and severe obesity (OR: 2.57, 95% CI: 1.31–5.05). Active cancer (OR: 1.46, 95% CI: 1.04–2.04) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles. Conclusions Based on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.
@article{10.1371/journal.pone.0247461,
abstract = {Aim COVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19. Methods The systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1st January 2020 and 9th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable. Results Seventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR: 2.65, 95% CI: 1.81–3.90), male sex (OR: 2.05, 95% CI: 1.39–3.04) and severe obesity (OR: 2.57, 95% CI: 1.31–5.05). Active cancer (OR: 1.46, 95% CI: 1.04–2.04) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles. Conclusions Based on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.},
author = {Booth, Adam and Reed, Angus Bruno and Ponzo, Sonia and Yassaee, Arrash and Aral, Mert and Plans, David and Labrique, Alain and Mohan, Diwakar},
journal = {PLOS ONE},
keywords = {l3s},
month = {03},
number = 3,
pages = {1-30},
publisher = {Public Library of Science},
title = {Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis},
volume = 16,
year = 2021
}%0 Journal Article
%1 10.1371/journal.pone.0247461
%A Booth, Adam
%A Reed, Angus Bruno
%A Ponzo, Sonia
%A Yassaee, Arrash
%A Aral, Mert
%A Plans, David
%A Labrique, Alain
%A Mohan, Diwakar
%D 2021
%I Public Library of Science
%J PLOS ONE
%N 3
%P 1-30
%R 10.1371/journal.pone.0247461
%T Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis
%U https://doi.org/10.1371/journal.pone.0247461
%V 16
%X Aim COVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19. Methods The systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1st January 2020 and 9th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable. Results Seventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR: 2.65, 95% CI: 1.81–3.90), male sex (OR: 2.05, 95% CI: 1.39–3.04) and severe obesity (OR: 2.57, 95% CI: 1.31–5.05). Active cancer (OR: 1.46, 95% CI: 1.04–2.04) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles. Conclusions Based on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements. - Tan, D. W., Gilani, S. Z., Boutrus, M., Alvares, G. A., Whitehouse, A. J., Mian, A., Suter, D., and Maybery, M. T. (2021)Facial asymmetry in parents of children on the autism spectrum, Autism Research.
@article{autism:2021,
author = {Tan, D W and Gilani, S Z and Boutrus, M and Alvares, G A. and Whitehouse, A J.O. and Mian, A and Suter, D and Maybery, M T.},
journal = {Autism Research},
keywords = {leibnizailab},
title = {Facial asymmetry in parents of children on the autism spectrum},
year = 2021
}%0 Journal Article
%1 autism:2021
%A Tan, D W
%A Gilani, S Z
%A Boutrus, M
%A Alvares, G A.
%A Whitehouse, A J.O.
%A Mian, A
%A Suter, D
%A Maybery, M T.
%D 2021
%J Autism Research
%R 10.1002/aur.2612
%T Facial asymmetry in parents of children on the autism spectrum - Ghosh, S., Ganguly, N., Mitra, B., and De, P. (2021)Designing an Experience Sampling Method for Smartphone Based Emotion Detection, IEEE Transactions on Affective Computing 12, 913–927.Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus, a good ESM design must consider -
probing frequency ,timely self-report collection , andnotifying at opportune moment to ensure highresponse quality . We propose a two-phase ESM design, where the first phase (a) balances between probing frequency and self-report timeliness, and (b) in parallel, constructs a predictive model to identify opportune probing moments. The second phase uses this model to further improve response quality by eliminating inopportune probes. We use typing-based emotion detection in smartphone as a case study to validate proposed ESM design. Our results demonstrate that it reduces probing rate by 64 percent, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9 percent while detecting inopportune moments with an average accuracy of 89 percent. These design choices improve the response quality, as manifested by 96 percent valid response collection and a maximum improvement of 24 percent in emotion classification accuracy.@article{8668435,
abstract = {Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus, a good ESM design must consider -probing frequency ,timely self-report collection , andnotifying at opportune moment to ensure highresponse quality . We propose a two-phase ESM design, where the first phase (a) balances between probing frequency and self-report timeliness, and (b) in parallel, constructs a predictive model to identify opportune probing moments. The second phase uses this model to further improve response quality by eliminating inopportune probes. We use typing-based emotion detection in smartphone as a case study to validate proposed ESM design. Our results demonstrate that it reduces probing rate by 64 percent, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9 percent while detecting inopportune moments with an average accuracy of 89 percent. These design choices improve the response quality, as manifested by 96 percent valid response collection and a maximum improvement of 24 percent in emotion classification accuracy.},
author = {Ghosh, Surjya and Ganguly, Niloy and Mitra, Bivas and De, Pradipta},
journal = {IEEE Transactions on Affective Computing},
keywords = {leibnizailab},
month = 10,
number = 4,
pages = {913-927},
title = {Designing an Experience Sampling Method for Smartphone Based Emotion Detection},
volume = 12,
year = 2021
}%0 Journal Article
%1 8668435
%A Ghosh, Surjya
%A Ganguly, Niloy
%A Mitra, Bivas
%A De, Pradipta
%D 2021
%J IEEE Transactions on Affective Computing
%N 4
%P 913-927
%R 10.1109/TAFFC.2019.2905561
%T Designing an Experience Sampling Method for Smartphone Based Emotion Detection
%V 12
%X Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus, a good ESM design must consider -probing frequency ,timely self-report collection , andnotifying at opportune moment to ensure highresponse quality . We propose a two-phase ESM design, where the first phase (a) balances between probing frequency and self-report timeliness, and (b) in parallel, constructs a predictive model to identify opportune probing moments. The second phase uses this model to further improve response quality by eliminating inopportune probes. We use typing-based emotion detection in smartphone as a case study to validate proposed ESM design. Our results demonstrate that it reduces probing rate by 64 percent, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9 percent while detecting inopportune moments with an average accuracy of 89 percent. These design choices improve the response quality, as manifested by 96 percent valid response collection and a maximum improvement of 24 percent in emotion classification accuracy. - Roy, S., Chakraborty, S., Mandal, A., Balde, G., Sharma, P., Natarajan, A., Khosla, M., Sural, S., and Ganguly, N. (2021)Knowledge-Aware Neural Networks for Medical Forum Question Classification. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3398–3402, Association for Computing Machinery, New York, NY, USA.Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.
@inbook{10.1145/3459637.3482128,
abstract = {Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.},
address = {New York, NY, USA},
author = {Roy, Soumyadeep and Chakraborty, Sudip and Mandal, Aishik and Balde, Gunjan and Sharma, Prakhar and Natarajan, Anandhavelu and Khosla, Megha and Sural, Shamik and Ganguly, Niloy},
booktitle = {Proceedings of the 30th ACM International Conference on Information & Knowledge Management},
keywords = {l3s},
pages = {3398–3402},
publisher = {Association for Computing Machinery},
title = {Knowledge-Aware Neural Networks for Medical Forum Question Classification},
year = 2021
}%0 Book Section
%1 10.1145/3459637.3482128
%A Roy, Soumyadeep
%A Chakraborty, Sudip
%A Mandal, Aishik
%A Balde, Gunjan
%A Sharma, Prakhar
%A Natarajan, Anandhavelu
%A Khosla, Megha
%A Sural, Shamik
%A Ganguly, Niloy
%B Proceedings of the 30th ACM International Conference on Information & Knowledge Management
%C New York, NY, USA
%D 2021
%I Association for Computing Machinery
%P 3398–3402
%T Knowledge-Aware Neural Networks for Medical Forum Question Classification
%U https://doi.org/10.1145/3459637.3482128
%X Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.
%@ 9781450384469 - Singh, J., Wang, Z., Khosla, M., and Anand, A. (2021)Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models. In International Conference on the Theory of Information Retrieval.
@inproceedings{noauthororeditor2021extracting,
author = {Singh, Jaspreet and Wang, Zhenye and Khosla, Megha and Anand, Avishek},
booktitle = {International Conference on the Theory of Information Retrieval},
keywords = {leibnizailab},
title = {Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models},
year = 2021
}%0 Conference Paper
%1 noauthororeditor2021extracting
%A Singh, Jaspreet
%A Wang, Zhenye
%A Khosla, Megha
%A Anand, Avishek
%B International Conference on the Theory of Information Retrieval
%D 2021
%T Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models - Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., and Bischl, B. (2021)Explaining Hyperparameter Optimization via Partial Dependence Plots. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS).
@inproceedings{MooHer2021a,
author = {Moosbauer, Julia and Herbinger, Julia and Casalicchio, Giuseppe and Lindauer, Marius and Bischl, Bernd},
booktitle = {Proceedings of the international conference on Neural Information Processing Systems (NeurIPS)},
keywords = {Plots},
month = 12,
title = {Explaining Hyperparameter Optimization via Partial Dependence Plots},
year = 2021
}%0 Conference Paper
%1 MooHer2021a
%A Moosbauer, Julia
%A Herbinger, Julia
%A Casalicchio, Giuseppe
%A Lindauer, Marius
%A Bischl, Bernd
%B Proceedings of the international conference on Neural Information Processing Systems (NeurIPS)
%D 2021
%T Explaining Hyperparameter Optimization via Partial Dependence Plots - Zhao, B., van der Aa, H., Nguyen, T. T., Nguyen, Q. V. H., and Weidlich, M. (2021){EIRES}: Efficient Integration of Remote Data in Event Stream Processing. In Proceedings of the 2021 International Conference on Management of Data, {ACM}.To support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data.
@inproceedings{Zhao_2021,
abstract = {To support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data.},
author = {Zhao, Bo and van der Aa, Han and Nguyen, Thanh Tam and Nguyen, Quoc Viet Hung and Weidlich, Matthias},
booktitle = {Proceedings of the 2021 International Conference on Management of Data},
keywords = {l3s},
month = {06},
publisher = {{ACM}},
title = {{EIRES}: Efficient Integration of Remote Data in Event Stream Processing},
year = 2021
}%0 Conference Paper
%1 Zhao_2021
%A Zhao, Bo
%A van der Aa, Han
%A Nguyen, Thanh Tam
%A Nguyen, Quoc Viet Hung
%A Weidlich, Matthias
%B Proceedings of the 2021 International Conference on Management of Data
%D 2021
%I {ACM}
%R 10.1145/3448016.3457304
%T {EIRES}: Efficient Integration of Remote Data in Event Stream Processing
%U https://doi.org/10.1145%2F3448016.3457304
%X To support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data. - Hinrichs, R., Gajecki, T., Ostermann, J., and Nogueira, W. (2021)A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants, Journal of the Acoustic Society of America.
@article{HinGaj2021,
author = {Hinrichs, Reemt and Gajecki, Tom and Ostermann, J{ö}rn and Nogueira, Waldo},
journal = {Journal of the Acoustic Society of America},
keywords = {stimulation},
month = {03},
title = {A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants},
year = 2021
}%0 Journal Article
%1 HinGaj2021
%A Hinrichs, Reemt
%A Gajecki, Tom
%A Ostermann, J{ö}rn
%A Nogueira, Waldo
%D 2021
%J Journal of the Acoustic Society of America
%T A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants - Perez-Liebana, D., Guerrero-Romero, C., Dockhorn, A., Xu, L., Hurtado, J., and Jeurissen, D. (2021)Generating Diverse and Competitive Play-Styles for Strategy Games. In 2021 IEEE Conference on Games (CoG), pp. 1–8.
@inproceedings{PerGue2021a,
author = {Perez-Liebana, Diego and Guerrero-Romero, Cristina and Dockhorn, Alexander and Xu, Linjie and Hurtado, Jorge and Jeurissen, Dominik},
booktitle = {2021 IEEE Conference on Games (CoG)},
keywords = {Diverse},
pages = {1-8},
title = {Generating Diverse and Competitive Play-Styles for Strategy Games},
year = 2021
}%0 Conference Paper
%1 PerGue2021a
%A Perez-Liebana, Diego
%A Guerrero-Romero, Cristina
%A Dockhorn, Alexander
%A Xu, Linjie
%A Hurtado, Jorge
%A Jeurissen, Dominik
%B 2021 IEEE Conference on Games (CoG)
%D 2021
%P 1-8
%R 10.1109/CoG52621.2021.9619094
%T Generating Diverse and Competitive Play-Styles for Strategy Games
%U https://ieeexplore.ieee.org/document/9619094
%@ 978-1-6654-3886-5 - Adhisantoso, Y. G., and Ostermann, J. (2021)Efficient Coding of Contact Matrices m57789, ISO/IEC JTC 1/SC 29/WG 8.
@article{AdhOst2021b,
author = {Adhisantoso, Yeremia Gunawan and Ostermann, J{ö}rn},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {m57789},
month = 10,
title = {Efficient Coding of Contact Matrices m57789},
year = 2021
}%0 Journal Article
%1 AdhOst2021b
%A Adhisantoso, Yeremia Gunawan
%A Ostermann, J{ö}rn
%D 2021
%J ISO/IEC JTC 1/SC 29/WG 8
%T Efficient Coding of Contact Matrices m57789 - Apeldoorn, D., and Dockhorn, A. (2021)Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning, IEEE Transactions on Games 13, 249–262.
@article{ApeDoc2021,
author = {Apeldoorn, Daan and Dockhorn, Alexander},
journal = {IEEE Transactions on Games},
keywords = {Knowledge},
number = 3,
pages = {249-262},
title = {Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning},
volume = 13,
year = 2021
}%0 Journal Article
%1 ApeDoc2021
%A Apeldoorn, Daan
%A Dockhorn, Alexander
%D 2021
%J IEEE Transactions on Games
%N 3
%P 249-262
%R 10.1109/TG.2020.3008002
%T Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning
%U https://ieeexplore.ieee.org/document/9136897/
%V 13
2020
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@article{KraKoc2020a,
author = {Krause, Lutz and Koc, Julian and Rosenhahn, Bodo and Rosenhahn, Axel},
journal = {Environmental Science and Technology},
keywords = {Neural},
number = 16,
pages = {10022-10030},
title = {Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure},
volume = 54,
year = 2020
}%0 Journal Article
%1 KraKoc2020a
%A Krause, Lutz
%A Koc, Julian
%A Rosenhahn, Bodo
%A Rosenhahn, Axel
%D 2020
%J Environmental Science and Technology
%N 16
%P 10022-10030
%R 10.1021/acs.est.0c01982
%T Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure
%U https://pubs.acs.org/doi/10.1021/acs.est.0c01982
%V 54 - Dockhorn, A., and Kruse, R. (2020)Predicting Cards Using a Fuzzy Multiset Clustering of Decks, International Journal of Computational Intelligence Systems (IJCIS) 13, 1207–1217.
@article{DocKru2020,
author = {Dockhorn, Alexander and Kruse, Rudolf},
journal = {International Journal of Computational Intelligence Systems (IJCIS)},
keywords = {Cards},
month = {08},
number = 1,
pages = {1207--1217},
title = {Predicting Cards Using a Fuzzy Multiset Clustering of Decks},
volume = 13,
year = 2020
}%0 Journal Article
%1 DocKru2020
%A Dockhorn, Alexander
%A Kruse, Rudolf
%D 2020
%J International Journal of Computational Intelligence Systems (IJCIS)
%N 1
%P 1207--1217
%R 10.2991/ijcis.d.200805.001
%T Predicting Cards Using a Fuzzy Multiset Clustering of Decks
%U https://www.atlantis-press.com/journals/ijcis/125943384/view
%V 13 - Ackermann, H., Meuel, H., Rosenhahn, B., and Ostermann, J. (2020)Verfahren und Vorrichtung zum Aufnehmen eines Digitalbildes 1–12.
@article{AckMeu2020,
author = {Ackermann, Hanno and Meuel, Holger and Rosenhahn, Bodo and Ostermann, J{ö}rn},
keywords = {Digitalbild},
month = {08},
pages = {1-12},
title = {Verfahren und Vorrichtung zum Aufnehmen eines Digitalbildes},
year = 2020
}%0 Journal Article
%1 AckMeu2020
%A Ackermann, Hanno
%A Meuel, Holger
%A Rosenhahn, Bodo
%A Ostermann, J{ö}rn
%D 2020
%P 1-12
%T Verfahren und Vorrichtung zum Aufnehmen eines Digitalbildes
%U https://register.dpma.de/DPMAregister/pat/register?AKZ=1020171297707\&CURSOR=0 - Zimmer, L., Lindauer, M., and Hutter, F. (2020)Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. In arxiv:2006.13799[cs.LG].
@inproceedings{ZimLin2020,
author = {Zimmer, Lucas and Lindauer, Marius and Hutter, Frank},
booktitle = {arxiv:2006.13799[cs.LG]},
keywords = {Auto-PyTorch},
month = {06},
title = {Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL},
year = 2020
}%0 Conference Paper
%1 ZimLin2020
%A Zimmer, Lucas
%A Lindauer, Marius
%A Hutter, Frank
%B arxiv:2006.13799[cs.LG]
%D 2020
%T Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
%U https://arxiv.org/abs/2006.13799 - Wallat, J., Singh, J., and Anand, A. (2020)BERTnesia: Investigating the capture and forgetting of knowledge in BERT. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP@EMNLP 2020, Online, November 2020 (Alishahi, A., Belinkov, Y., Chrupala, G., Hupkes, D., Pinter, Y., and Sajjad, H., Eds.), pp. 174–183, Association for Computational Linguistics.
@inproceedings{DBLP:conf/blackboxnlp/SinghWA20,
author = {Wallat, Jonas and Singh, Jaspreet and Anand, Avishek},
booktitle = {Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP@EMNLP 2020, Online, November 2020},
editor = {Alishahi, Afra and Belinkov, Yonatan and Chrupala, Grzegorz and Hupkes, Dieuwke and Pinter, Yuval and Sajjad, Hassan},
keywords = {leibnizailab},
pages = {174--183},
publisher = {Association for Computational Linguistics},
title = {BERTnesia: Investigating the capture and forgetting of knowledge in BERT},
year = 2020
}%0 Conference Paper
%1 DBLP:conf/blackboxnlp/SinghWA20
%A Wallat, Jonas
%A Singh, Jaspreet
%A Anand, Avishek
%B Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP@EMNLP 2020, Online, November 2020
%D 2020
%E Alishahi, Afra
%E Belinkov, Yonatan
%E Chrupala, Grzegorz
%E Hupkes, Dieuwke
%E Pinter, Yuval
%E Sajjad, Hassan
%I Association for Computational Linguistics
%P 174--183
%R 10.18653/V1/2020.BLACKBOXNLP-1.17
%T BERTnesia: Investigating the capture and forgetting of knowledge in BERT
%U https://doi.org/10.18653/v1/2020.blackboxnlp-1.17 - Cong, Y., Ackermann, H., Liao, W., Yang, M. Y., and Rosenhahn, B. (2020)NODIS: Neural Ordinary Differential Scene Understanding. In European Conference on Computer Vision (ECCV).
@inproceedings{ConAck2020,
author = {Cong, Yuren and Ackermann, Hanno and Liao, Wentong and Yang, Michael Ying and Rosenhahn, Bodo},
booktitle = {European Conference on Computer Vision (ECCV)},
keywords = {NODIS},
month = {08},
title = {NODIS: Neural Ordinary Differential Scene Understanding},
year = 2020
}%0 Conference Paper
%1 ConAck2020
%A Cong, Yuren
%A Ackermann, Hanno
%A Liao, Wentong
%A Yang, Michael Ying
%A Rosenhahn, Bodo
%B European Conference on Computer Vision (ECCV)
%D 2020
%T NODIS: Neural Ordinary Differential Scene Understanding
%U https://arxiv.org/abs/2001.04735v2 - Hu, T., Iosifidis, V., Liao, W., Zhang, H., Yang, M. Y., Ntoutsi, E., and Rosenhahn, B. (2020)FairNN - Conjoint Learning of Fair Representations for Fair Decisions.. In Discovery Science, pp. 581–595, Springer International Publishing.
@incollection{Hu_2020,
author = {Hu, Tongxin and Iosifidis, Vasileios and Liao, Wentong and Zhang, Hang and Yang, Michael Ying and Ntoutsi, Eirini and Rosenhahn, Bodo},
booktitle = {Discovery Science},
keywords = {l3s},
pages = {581-595},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
title = {FairNN - Conjoint Learning of Fair Representations for Fair Decisions.},
type = {Publication},
volume = 12323,
year = 2020
}%0 Book Section
%1 Hu_2020
%A Hu, Tongxin
%A Iosifidis, Vasileios
%A Liao, Wentong
%A Zhang, Hang
%A Yang, Michael Ying
%A Ntoutsi, Eirini
%A Rosenhahn, Bodo
%B Discovery Science
%D 2020
%I Springer International Publishing
%P 581-595
%R 10.1007/978-3-030-61527-7_38
%T FairNN - Conjoint Learning of Fair Representations for Fair Decisions.
%U https://doi.org/10.1007%2F978-3-030-61527-7_38
%V 12323 - Hornakova*, A., Henschel*, R., Rosenhahn, B., Swoboda, P., and equal contribution), (*. (2020)Lifted Disjoint Paths with Application in Multiple Object Tracking, Proceedings of the 37th International Conference on Machine Learning (ICML).
@article{HorHen2020,
author = {Hornakova*, Andrea and Henschel*, Roberto and Rosenhahn, Bodo and Swoboda, Paul and equal contribution), (*},
journal = {Proceedings of the 37th International Conference on Machine Learning (ICML)},
keywords = {Disjoint},
month = {07},
title = {Lifted Disjoint Paths with Application in Multiple Object Tracking},
year = 2020
}%0 Journal Article
%1 HorHen2020
%A Hornakova*, Andrea
%A Henschel*, Roberto
%A Rosenhahn, Bodo
%A Swoboda, Paul
%A equal contribution), (*
%D 2020
%J Proceedings of the 37th International Conference on Machine Learning (ICML)
%T Lifted Disjoint Paths with Application in Multiple Object Tracking
%U /brokenurl# Visual Results, Code, Video Presentation - Samayoa, Y., and Ostermann, J. (2020)Modified Active Constellation Extension Algorithm for PAPR Reduction in OFDM Systems. In 2020 Wireless Telecommunications Symposium (WTS), p. 5.
@inproceedings{SamOst2020a,
author = {Samayoa, Yasser and Ostermann, J{ö}rn},
booktitle = {2020 Wireless Telecommunications Symposium (WTS)},
keywords = {OFDM},
month = {04},
pages = 5,
title = {Modified Active Constellation Extension Algorithm for PAPR Reduction in OFDM Systems},
year = 2020
}%0 Conference Paper
%1 SamOst2020a
%A Samayoa, Yasser
%A Ostermann, J{ö}rn
%B 2020 Wireless Telecommunications Symposium (WTS)
%D 2020
%P 5
%T Modified Active Constellation Extension Algorithm for PAPR Reduction in OFDM Systems - Rudolph, M., Wandt, B., and Rosenhahn, B. (2020, August)Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows..The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question. To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows. Normalizing flows are well-suited to deal with low dimensional data distributions. However, they struggle with the high dimensionality of images. Therefore, we employ a multi-scale feature extractor which enables the normalizing flow to assign meaningful likelihoods to the images. Based on these likelihoods we develop a scoring function that indicates defects. Moreover, propagating the score back to the image enables pixel-wise localization. To achieve a high robustness and performance we exploit multiple transformations in training and evaluation. In contrast to most other methods, ours does not require a large number of training samples and performs well with as low as 16 images. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD and Magnetic Tile Defects datasets.
@misc{rudolph2020differnet,
abstract = {The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question. To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows. Normalizing flows are well-suited to deal with low dimensional data distributions. However, they struggle with the high dimensionality of images. Therefore, we employ a multi-scale feature extractor which enables the normalizing flow to assign meaningful likelihoods to the images. Based on these likelihoods we develop a scoring function that indicates defects. Moreover, propagating the score back to the image enables pixel-wise localization. To achieve a high robustness and performance we exploit multiple transformations in training and evaluation. In contrast to most other methods, ours does not require a large number of training samples and performs well with as low as 16 images. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD and Magnetic Tile Defects datasets.},
author = {Rudolph, Marco and Wandt, Bastian and Rosenhahn, Bodo},
keywords = {l3s},
month = {08},
note = {cite arxiv:2008.12577},
title = {Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows.},
year = 2020
}%0 Generic
%1 rudolph2020differnet
%A Rudolph, Marco
%A Wandt, Bastian
%A Rosenhahn, Bodo
%D 2020
%T Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows.
%U http://arxiv.org/abs/2008.12577
%X The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question. To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows. Normalizing flows are well-suited to deal with low dimensional data distributions. However, they struggle with the high dimensionality of images. Therefore, we employ a multi-scale feature extractor which enables the normalizing flow to assign meaningful likelihoods to the images. Based on these likelihoods we develop a scoring function that indicates defects. Moreover, propagating the score back to the image enables pixel-wise localization. To achieve a high robustness and performance we exploit multiple transformations in training and evaluation. In contrast to most other methods, ours does not require a large number of training samples and performs well with as low as 16 images. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD and Magnetic Tile Defects datasets. - Meuel, H., and Ostermann, J. (2020)Analysis of Affine Motion-Compensated Prediction in Video Coding, IEEE Transactions on Image Processing 29, 7359–7374.
@article{MeuOst2020,
author = {Meuel, Holger and Ostermann, J{ö}rn},
journal = {IEEE Transactions on Image Processing},
keywords = {Video},
month = {06},
note = {Orchid iD: 0000-0002-1275-303X},
pages = {7359-7374},
title = {Analysis of Affine Motion-Compensated Prediction in Video Coding},
volume = 29,
year = 2020
}%0 Journal Article
%1 MeuOst2020
%A Meuel, Holger
%A Ostermann, J{ö}rn
%D 2020
%J IEEE Transactions on Image Processing
%P 7359-7374
%R 10.1109/TIP.2020.3001734
%T Analysis of Affine Motion-Compensated Prediction in Video Coding
%U https://ieeexplore.ieee.org/document/9119829
%V 29 - Adhisantoso, Y. G., Rohlfing, C., Voges, J., and Ostermann, J. (2020)Method for the coding of genotype likelihood of variant m55356, ISO/IEC JTC 1/SC 29/WG 8.
@article{AdhR2020b,
author = {Adhisantoso, Yeremia Gunawan and Rohlfing, Christian and Voges, Jan and Ostermann, J{ö}rn},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {genotype},
month = 10,
title = {Method for the coding of genotype likelihood of variant m55356},
year = 2020
}%0 Journal Article
%1 AdhR2020b
%A Adhisantoso, Yeremia Gunawan
%A Rohlfing, Christian
%A Voges, Jan
%A Ostermann, J{ö}rn
%D 2020
%J ISO/IEC JTC 1/SC 29/WG 8
%T Method for the coding of genotype likelihood of variant m55356 - Sen, H., Wentong, L., Tavakoli, H. R., Yang, M. Y., Rosenhahn, B., and Pugeault, N. (2020)Image Captioning through Image Transformer. In Asian Conference on Computer Vision (ACCV).
@inproceedings{HeSLia2020,
author = {Sen, He and Wentong, Liao and Tavakoli, Hamed Rezazadegan and Yang, Michael Ying and Rosenhahn, Bodo and Pugeault, Nicolas},
booktitle = {Asian Conference on Computer Vision (ACCV)},
keywords = {Image},
month = 11,
title = {Image Captioning through Image Transformer},
year = 2020
}%0 Conference Paper
%1 HeSLia2020
%A Sen, He
%A Wentong, Liao
%A Tavakoli, Hamed Rezazadegan
%A Yang, Michael Ying
%A Rosenhahn, Bodo
%A Pugeault, Nicolas
%B Asian Conference on Computer Vision (ACCV)
%D 2020
%T Image Captioning through Image Transformer - Hartmann, F., Sommer, A., Pestel-Schiller, U., and Osterman, J. (2020)A scheme for stabilizing the image generation for VideoSAR. In 13th European Conference on Synthetic Aperture Radar.
@inproceedings{HarSom2020a,
author = {Hartmann, Fabian and Sommer, Aron and Pestel-Schiller, Ulrike and Osterman, J{ö}rn},
booktitle = {13th European Conference on Synthetic Aperture Radar},
keywords = {leibnizailab},
month = {06},
title = {A scheme for stabilizing the image generation for VideoSAR},
year = 2020
}%0 Conference Paper
%1 HarSom2020a
%A Hartmann, Fabian
%A Sommer, Aron
%A Pestel-Schiller, Ulrike
%A Osterman, J{ö}rn
%B 13th European Conference on Synthetic Aperture Radar
%D 2020
%T A scheme for stabilizing the image generation for VideoSAR - Liu, Z., Pavao, A., Xu, Z., Escalera, S., Ferreira, F., Guyon, I., Hong, S., Hutter, F., Ji, R., Jacques, J., Li, G., Lindauer, M., Luo, Z., Madadi, M., Nierhoff, T., Niu, K., Pan, C., Stoll, D., Treguer, S., Wang, J., Wang, P., Wu, C., Xiong, Y., Zela, A., and Zhang, Y. (2020)Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019. In HAL.
@inproceedings{LiuPav2020,
author = {Liu, Zhengying and Pavao, Adrien and Xu, Zhen and Escalera, Sergio and Ferreira, Fabio and Guyon, Isabelle and Hong, Sirui and Hutter, Frank and Ji, Rongrong and Jacques, Julio and Li, Ge and Lindauer, Marius and Luo, Zhipeng and Madadi, Meysam and Nierhoff, Thomas and Niu, Kangning and Pan, Chunguang and Stoll, Danny and Treguer, Sebastien and Wang, Jin and Wang, Peng and Wu, Chenglin and Xiong, Youcheng and Zela, Arbër and Zhang, Yang},
booktitle = {HAL},
keywords = {ChaLearn},
month = {09},
title = {Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019},
year = 2020
}%0 Conference Paper
%1 LiuPav2020
%A Liu, Zhengying
%A Pavao, Adrien
%A Xu, Zhen
%A Escalera, Sergio
%A Ferreira, Fabio
%A Guyon, Isabelle
%A Hong, Sirui
%A Hutter, Frank
%A Ji, Rongrong
%A Jacques, Julio
%A Li, Ge
%A Lindauer, Marius
%A Luo, Zhipeng
%A Madadi, Meysam
%A Nierhoff, Thomas
%A Niu, Kangning
%A Pan, Chunguang
%A Stoll, Danny
%A Treguer, Sebastien
%A Wang, Jin
%A Wang, Peng
%A Wu, Chenglin
%A Xiong, Youcheng
%A Zela, Arbër
%A Zhang, Yang
%B HAL
%D 2020
%T Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
%U https://hal.archives-ouvertes.fr/hal-02957135 - Gaina, R. D., Balla, M., Dockhorn, A., Montoliu, R., and Perez liebana, D. (2020)TAG : A Tabletop Games Framework. In Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); CEUR Workshop Proceedings (2020), pp. 1–7.
@inproceedings{GaiBal2020,
author = {Gaina, Raluca D and Balla, Martin and Dockhorn, Alexander and Montoliu, Raul and Perez liebana, Diego},
booktitle = {Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); CEUR Workshop Proceedings (2020)},
keywords = {TAG},
pages = {1--7},
title = {TAG : A Tabletop Games Framework},
year = 2020
}%0 Conference Paper
%1 GaiBal2020
%A Gaina, Raluca D
%A Balla, Martin
%A Dockhorn, Alexander
%A Montoliu, Raul
%A Perez liebana, Diego
%B Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); CEUR Workshop Proceedings (2020)
%D 2020
%P 1--7
%T TAG : A Tabletop Games Framework
%U http://ceur-ws.org/Vol-2862/ - Voges, J., Paridaens, T., M{ü}ntefering, F., Mainzer, L. S., Bliss, B., Yang, M., Ochoa, I., Fostier, J., Ostermann, J., and Hernaez, M. (2020)GABAC: an arithmetic coding solution for genomic data, Bioinformatics 36, 2275–2277.
@article{VogPar2020a,
author = {Voges, Jan and Paridaens, Tom and M{ü}ntefering, Fabian and Mainzer, Liudmila S. and Bliss, Brian and Yang, Mingyu and Ochoa, Idoia and Fostier, Jan and Ostermann, J{ö}rn and Hernaez, Mikel},
journal = {Bioinformatics},
keywords = {arithmetic},
number = 7,
pages = {2275-2277},
title = {GABAC: an arithmetic coding solution for genomic data},
volume = 36,
year = 2020
}%0 Journal Article
%1 VogPar2020a
%A Voges, Jan
%A Paridaens, Tom
%A M{ü}ntefering, Fabian
%A Mainzer, Liudmila S.
%A Bliss, Brian
%A Yang, Mingyu
%A Ochoa, Idoia
%A Fostier, Jan
%A Ostermann, J{ö}rn
%A Hernaez, Mikel
%D 2020
%J Bioinformatics
%N 7
%P 2275-2277
%R 10.1093/bioinformatics/btz922
%T GABAC: an arithmetic coding solution for genomic data
%U https://doi.org/10.1093/bioinformatics/btz922
%V 36 - Awiszus, M., Schubert, F., and Rosenhahn, B. (2020, October)TOAD-GAN: Coherent Style Level Generation from a Single Example.In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.
@misc{awiszus2020toadgan,
abstract = {In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.},
author = {Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
keywords = {l3s},
month = 10,
note = {cite arxiv:2008.01531Comment: 7 pages, 7 figures. AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) 2020},
title = {TOAD-GAN: Coherent Style Level Generation from a Single Example},
year = 2020
}%0 Generic
%1 awiszus2020toadgan
%A Awiszus, Maren
%A Schubert, Frederik
%A Rosenhahn, Bodo
%D 2020
%T TOAD-GAN: Coherent Style Level Generation from a Single Example
%U http://arxiv.org/abs/2008.01531
%X In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code. - Perez-Liebana, D., Dockhorn, A., Grueso, J. H., and Jeurissen, D. (2020)The Design Of “Stratega”: A General Strategy Games Framework, arXiv:2009.05643 1–7.
@article{PerDoc2020,
author = {Perez-Liebana, Diego and Dockhorn, Alexander and Grueso, Jorge Hurtado and Jeurissen, Dominik},
journal = {arXiv:2009.05643},
keywords = {Of},
pages = {1--7},
title = {The Design Of “Stratega”: A General Strategy Games Framework},
year = 2020
}%0 Journal Article
%1 PerDoc2020
%A Perez-Liebana, Diego
%A Dockhorn, Alexander
%A Grueso, Jorge Hurtado
%A Jeurissen, Dominik
%D 2020
%J arXiv:2009.05643
%P 1--7
%T The Design Of “Stratega”: A General Strategy Games Framework
%U http://arxiv.org/abs/2009.05643 - Gebauer, C., and Bennewitz, M. (2020)Penalized Bootstrapping for Reinforcement Learning in Robot Control. In International Conference on Machine Learning and Applications (CMLA).
@inproceedings{GebBen2020,
author = {Gebauer, Christopher and Bennewitz, Maren},
booktitle = {International Conference on Machine Learning and Applications (CMLA)},
keywords = {Bootstrapping},
title = {Penalized Bootstrapping for Reinforcement Learning in Robot Control},
year = 2020
}%0 Conference Paper
%1 GebBen2020
%A Gebauer, Christopher
%A Bennewitz, Maren
%B International Conference on Machine Learning and Applications (CMLA)
%D 2020
%T Penalized Bootstrapping for Reinforcement Learning in Robot Control - Luo, C., Zhao, P., Chen, C., Qiao, B., Du, C., Zhang, H., Wu, W., Cai, S., He, B., Rajmohan, S., and Lin, Q. (2020)PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector. In , pp. 8784–8792.Positive-unlabeled learning (PU learning) is an important case of binary classification where the training data only contains positive and unlabeled samples. The current state-of-the-art approach for PU learning is the cost-sensitive approach, which casts PU learning as a cost-sensitive classification problem and relies on unbiased risk estimator for correcting the bias introduced by the unlabeled samples. However, this approach requires the knowledge of class prior and is subject to the potential label noise. In this paper, we propose a novel PU learning approach dubbed PULNS, equipped with an effective negative sample selector, which is optimized by reinforcement learning. Our PULNS approach employs an effective negative sample selector as the agent responsible for selecting negative samples from the unlabeled data. While the selected, likely negative samples can be used to improve the classifier, the performance of classifier is also used as the reward to improve the selector through the REINFORCE algorithm. By alternating the updates of the selector and the classifier, the performance of both is improved. Extensive experimental studies on 7 real-world application benchmarks demonstrate that PULNS consistently outperforms the current state-of-the-art methods in PU learning, and our experimental results also confirm the effectiveness of the negative sample selector underlying PULNS.
@inproceedings{luo20212020pulns,
abstract = {Positive-unlabeled learning (PU learning) is an important case of binary classification where the training data only contains positive and unlabeled samples. The current state-of-the-art approach for PU learning is the cost-sensitive approach, which casts PU learning as a cost-sensitive classification problem and relies on unbiased risk estimator for correcting the bias introduced by the unlabeled samples. However, this approach requires the knowledge of class prior and is subject to the potential label noise. In this paper, we propose a novel PU learning approach dubbed PULNS, equipped with an effective negative sample selector, which is optimized by reinforcement learning. Our PULNS approach employs an effective negative sample selector as the agent responsible for selecting negative samples from the unlabeled data. While the selected, likely negative samples can be used to improve the classifier, the performance of classifier is also used as the reward to improve the selector through the REINFORCE algorithm. By alternating the updates of the selector and the classifier, the performance of both is improved. Extensive experimental studies on 7 real-world application benchmarks demonstrate that PULNS consistently outperforms the current state-of-the-art methods in PU learning, and our experimental results also confirm the effectiveness of the negative sample selector underlying PULNS.},
author = {Luo, Chuan and Zhao, Pu and Chen, Chen and Qiao, Bo and Du, Chao and Zhang, Hongyu and Wu, Wei and Cai, Shaowei and He, Bing and Rajmohan, Saravanakumar and Lin, Qingwei},
keywords = {l3s},
number = 10,
pages = {8784-8792},
title = {PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector},
type = {Publication},
volume = 35,
year = 2020
}%0 Conference Paper
%1 luo20212020pulns
%A Luo, Chuan
%A Zhao, Pu
%A Chen, Chen
%A Qiao, Bo
%A Du, Chao
%A Zhang, Hongyu
%A Wu, Wei
%A Cai, Shaowei
%A He, Bing
%A Rajmohan, Saravanakumar
%A Lin, Qingwei
%D 2020
%N 10
%P 8784-8792
%T PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector
%U https://ojs.aaai.org/index.php/AAAI/article/view/17064
%V 35
%X Positive-unlabeled learning (PU learning) is an important case of binary classification where the training data only contains positive and unlabeled samples. The current state-of-the-art approach for PU learning is the cost-sensitive approach, which casts PU learning as a cost-sensitive classification problem and relies on unbiased risk estimator for correcting the bias introduced by the unlabeled samples. However, this approach requires the knowledge of class prior and is subject to the potential label noise. In this paper, we propose a novel PU learning approach dubbed PULNS, equipped with an effective negative sample selector, which is optimized by reinforcement learning. Our PULNS approach employs an effective negative sample selector as the agent responsible for selecting negative samples from the unlabeled data. While the selected, likely negative samples can be used to improve the classifier, the performance of classifier is also used as the reward to improve the selector through the REINFORCE algorithm. By alternating the updates of the selector and the classifier, the performance of both is improved. Extensive experimental studies on 7 real-world application benchmarks demonstrate that PULNS consistently outperforms the current state-of-the-art methods in PU learning, and our experimental results also confirm the effectiveness of the negative sample selector underlying PULNS. - Cheng, H., Liao, W., Ying, Y. M., Sester, M., and Rosenhahn, B. (2020)MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic. In 23rd International Conference on Intelligent Transportation Systems (ITSC).
@inproceedings{CheLia2020a,
author = {Cheng, Hao and Liao, Wentong and Ying, Yang Michael and Sester, Monica and Rosenhahn, Bodo},
booktitle = {23rd International Conference on Intelligent Transportation Systems (ITSC)},
keywords = {Multi-Context},
month = {09},
note = {In cooperation with Institut f{ü}r Kartographie und Geoinformatik, Uni Hannover, Germany},
title = {MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic},
year = 2020
}%0 Conference Paper
%1 CheLia2020a
%A Cheng, Hao
%A Liao, Wentong
%A Ying, Yang Michael
%A Sester, Monica
%A Rosenhahn, Bodo
%B 23rd International Conference on Intelligent Transportation Systems (ITSC)
%D 2020
%T MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic - Wallat, J., Singh, J., and Anand, A. (2020)BERTnesia: Investigating the capture and forgetting of knowledge in BERT.. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pp. 174–183, Association for Computational Linguistics, Online.Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this paper, we probe BERT specifically to understand and measure the relational knowledge it captures. We utilize knowledge base completion tasks to probe every layer of pre-trained as well as fine-tuned BERT (ranking, question answering, NER). Our findings show that knowledge is not just contained in BERT{'}s final layers. Intermediate layers contribute a significant amount (17-60{\%}) to the total knowledge found. Probing intermediate layers also reveals how different types of knowledge emerge at varying rates. When BERT is fine-tuned, relational knowledge is forgotten but the extent of forgetting is impacted by the fine-tuning objective but not the size of the dataset. We found that ranking models forget the least and retain more knowledge in their final layer.
@inproceedings{wallat-etal-2020-bertnesia,
abstract = {Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this paper, we probe BERT specifically to understand and measure the relational knowledge it captures. We utilize knowledge base completion tasks to probe every layer of pre-trained as well as fine-tuned BERT (ranking, question answering, NER). Our findings show that knowledge is not just contained in BERT{'}s final layers. Intermediate layers contribute a significant amount (17-60{\%}) to the total knowledge found. Probing intermediate layers also reveals how different types of knowledge emerge at varying rates. When BERT is fine-tuned, relational knowledge is forgotten but the extent of forgetting is impacted by the fine-tuning objective but not the size of the dataset. We found that ranking models forget the least and retain more knowledge in their final layer.},
address = {Online},
author = {Wallat, Jonas and Singh, Jaspreet and Anand, Avishek},
booktitle = {Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP},
keywords = {l3s},
month = 11,
pages = {174--183},
publisher = {Association for Computational Linguistics},
title = {BERTnesia: Investigating the capture and forgetting of knowledge in BERT.},
volume = {abs/2010.09313},
year = 2020
}%0 Conference Paper
%1 wallat-etal-2020-bertnesia
%A Wallat, Jonas
%A Singh, Jaspreet
%A Anand, Avishek
%B Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
%C Online
%D 2020
%I Association for Computational Linguistics
%P 174--183
%R 10.18653/v1/2020.blackboxnlp-1.17
%T BERTnesia: Investigating the capture and forgetting of knowledge in BERT.
%U http://dblp.uni-trier.de/db/journals/corr/corr2010.html#abs-2010-09313
%V abs/2010.09313
%X Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this paper, we probe BERT specifically to understand and measure the relational knowledge it captures. We utilize knowledge base completion tasks to probe every layer of pre-trained as well as fine-tuned BERT (ranking, question answering, NER). Our findings show that knowledge is not just contained in BERT{'}s final layers. Intermediate layers contribute a significant amount (17-60{\%}) to the total knowledge found. Probing intermediate layers also reveals how different types of knowledge emerge at varying rates. When BERT is fine-tuned, relational knowledge is forgotten but the extent of forgetting is impacted by the fine-tuning objective but not the size of the dataset. We found that ranking models forget the least and retain more knowledge in their final layer. - Krause, T., and Ostermann, J. (2020)Damage Detection for Wind Turbine Rotor Blades Using Airborne Sound, Structural Control and Health Monitoring.
@article{KraOst2020a,
author = {Krause, Thomas and Ostermann, J{ö}rn},
journal = {Structural Control and Health Monitoring},
keywords = {Wind},
month = {02},
title = {Damage Detection for Wind Turbine Rotor Blades Using Airborne Sound},
year = 2020
}%0 Journal Article
%1 KraOst2020a
%A Krause, Thomas
%A Ostermann, J{ö}rn
%D 2020
%J Structural Control and Health Monitoring
%R doi:10.1002/stc.2520
%T Damage Detection for Wind Turbine Rotor Blades Using Airborne Sound
%U https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2520 - Zell, P., Rosenhahn, B., and Wandt, B. (2020)Weakly-supervised Learning of Human Dynamics. In European Conference on Computer Vision (ECCV).
@inproceedings{ZelRos2020a,
author = {Zell, Petrissa and Rosenhahn, Bodo and Wandt, Bastian},
booktitle = {European Conference on Computer Vision (ECCV)},
keywords = {leibnizailab},
month = {08},
title = {Weakly-supervised Learning of Human Dynamics},
year = 2020
}%0 Conference Paper
%1 ZelRos2020a
%A Zell, Petrissa
%A Rosenhahn, Bodo
%A Wandt, Bastian
%B European Conference on Computer Vision (ECCV)
%D 2020
%T Weakly-supervised Learning of Human Dynamics
%U https://arxiv.org/abs/2007.08969 - Adhisantoso, Y. G., Rohlfing, C., Voges, J., and Ostermann, J. (2020)Extension to method for the coding of genomic variants m55355, ISO/IEC JTC 1/SC 29/WG 8.
@article{AdhR2020,
author = {Adhisantoso, Yeremia Gunawan and Rohlfing, Christian and Voges, Jan and Ostermann, J{ö}rn},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {genomic},
month = 10,
title = {Extension to method for the coding of genomic variants m55355},
year = 2020
}%0 Journal Article
%1 AdhR2020
%A Adhisantoso, Yeremia Gunawan
%A Rohlfing, Christian
%A Voges, Jan
%A Ostermann, J{ö}rn
%D 2020
%J ISO/IEC JTC 1/SC 29/WG 8
%T Extension to method for the coding of genomic variants m55355 - Eimer, T., Biedenkapp, A., Hutter, F., and Lindauer, M. (2020)Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning. In Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML’20).
@inproceedings{EimBie2020a,
author = {Eimer, Theresa and Biedenkapp, Andre and Hutter, Frank and Lindauer, Marius},
booktitle = {Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20)},
keywords = {Reinforcement},
month = {07},
title = {Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning},
year = 2020
}%0 Conference Paper
%1 EimBie2020a
%A Eimer, Theresa
%A Biedenkapp, Andre
%A Hutter, Frank
%A Lindauer, Marius
%B Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20)
%D 2020
%T Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning - Kluger, F., Brachmann, E., Ackermann, H., Rother, C., Yang, M. Y., and Rosenhahn, B. (2020)CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. In Computer Vision and Pattern Recognition (CVPR).
@inproceedings{KluBra2020,
author = {Kluger, Florian and Brachmann, Eric and Ackermann, Hanno and Rother, Carsten and Yang, Michael Ying and Rosenhahn, Bodo},
booktitle = {Computer Vision and Pattern Recognition (CVPR)},
keywords = {leibnizailab},
month = {06},
title = {CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus},
year = 2020
}%0 Conference Paper
%1 KluBra2020
%A Kluger, Florian
%A Brachmann, Eric
%A Ackermann, Hanno
%A Rother, Carsten
%A Yang, Michael Ying
%A Rosenhahn, Bodo
%B Computer Vision and Pattern Recognition (CVPR)
%D 2020
%T CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
%U https://arxiv.org/pdf/2001.02643.pdf - Benjak, M., and Ostermann, J. (2020)Applications suitable for AI-based data compression, 1st Meeting of ISO/IEC JTC 1/SC 29/WG 2 Document m55424.
@article{BenOst2020,
author = {Benjak, Martin and Ostermann, J{ö}rn},
journal = {1st Meeting of ISO/IEC JTC 1/SC 29/WG 2 Document m55424},
keywords = {compression},
title = {Applications suitable for AI-based data compression},
year = 2020
}%0 Journal Article
%1 BenOst2020
%A Benjak, Martin
%A Ostermann, J{ö}rn
%D 2020
%J 1st Meeting of ISO/IEC JTC 1/SC 29/WG 2 Document m55424
%T Applications suitable for AI-based data compression - {Fayyazifar}, N., {Ahderom}, S., {Suter}, D., {Maiorana}, A., and {Dwivedi}, G. (2020)Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals. In 2020 Computing in Cardiology, pp. 1–4.
@inproceedings{9344384,
author = {{Fayyazifar}, N. and {Ahderom}, S. and {Suter}, D. and {Maiorana}, A. and {Dwivedi}, G.},
booktitle = {2020 Computing in Cardiology},
keywords = {leibnizailab},
pages = {1-4},
title = {Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals},
year = 2020
}%0 Conference Paper
%1 9344384
%A {Fayyazifar}, N.
%A {Ahderom}, S.
%A {Suter}, D.
%A {Maiorana}, A.
%A {Dwivedi}, G.
%B 2020 Computing in Cardiology
%D 2020
%P 1-4
%R 10.22489/CinC.2020.161
%T Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals - Shala, G., Biedenkapp, A., Awad, N., Adriaensen, S., Lindauer, M., and Hutter, F. (2020)Learning Step-Size Adaptation in CMA-ES. In Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature ({PPSN}’20).
@inproceedings{ShaBie2020,
author = {Shala, Gresa and Biedenkapp, Andre and Awad, Noor and Adriaensen, Steven and Lindauer, Marius and Hutter, Frank},
booktitle = {Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature ({PPSN}'20)},
keywords = {CMA-ES},
month = {09},
title = {Learning Step-Size Adaptation in CMA-ES},
year = 2020
}%0 Conference Paper
%1 ShaBie2020
%A Shala, Gresa
%A Biedenkapp, Andre
%A Awad, Noor
%A Adriaensen, Steven
%A Lindauer, Marius
%A Hutter, Frank
%B Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature ({PPSN}'20)
%D 2020
%T Learning Step-Size Adaptation in CMA-ES
%U https://arxiv.org/abs/0805.0231 - Gritzner, D., and Ostermann, J. (2020)USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 483–492.
@article{GriOst2020,
author = {Gritzner, Daniel and Ostermann, J{ö}rn},
journal = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
keywords = {Aerial},
month = {08},
pages = {483--492},
title = {USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES},
year = 2020
}%0 Journal Article
%1 GriOst2020
%A Gritzner, Daniel
%A Ostermann, J{ö}rn
%D 2020
%J ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
%P 483--492
%R 10.5194/isprs-annals-V-2-2020-483-2020
%T USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES
%U https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/483/2020/ - Kluger, F., Ackermann, H., Yang, M. Y., and Rosenhahn, B. (2020)Temporally Consistent Horizon Lines. In International Conference on Robotics and Automation (ICRA).
@inproceedings{KluAck2020,
author = {Kluger, Florian and Ackermann, Hanno and Yang, Michael Ying and Rosenhahn, Bodo},
booktitle = {International Conference on Robotics and Automation (ICRA)},
keywords = {Lines},
month = {06},
note = {Accepted for publication},
title = {Temporally Consistent Horizon Lines},
year = 2020
}%0 Conference Paper
%1 KluAck2020
%A Kluger, Florian
%A Ackermann, Hanno
%A Yang, Michael Ying
%A Rosenhahn, Bodo
%B International Conference on Robotics and Automation (ICRA)
%D 2020
%T Temporally Consistent Horizon Lines
%U https://arxiv.org/pdf/1907.10014.pdf - Ostermann, J., and Hinrichs, R. (2020)Links und rechts verbinden, Unimagazin.
@article{OstHin2020a,
author = {Ostermann, J{ö}rn and Hinrichs, Reemt},
journal = {Unimagazin},
keywords = {und},
month = {06},
number = 1,
title = {Links und rechts verbinden},
year = 2020
}%0 Journal Article
%1 OstHin2020a
%A Ostermann, J{ö}rn
%A Hinrichs, Reemt
%D 2020
%J Unimagazin
%N 1
%T Links und rechts verbinden
%U https://anyflip.com/cjox/dool/ - Gaina, R. D., Balla, M., Dockhorn, A., Montoliu, R., and Perez-Liebana, D. (2020)Design and Implementation of TAG: A Tabletop Games Framework., arXiv:2009.12065.
@article{RalMar2020,
author = {Gaina, Raluca D. and Balla, Martin and Dockhorn, Alexander and Montoliu, Raul and Perez-Liebana, Diego},
journal = {arXiv:2009.12065},
keywords = {Implementation},
title = {Design and Implementation of TAG: A Tabletop Games Framework.},
year = 2020
}%0 Journal Article
%1 RalMar2020
%A Gaina, Raluca D.
%A Balla, Martin
%A Dockhorn, Alexander
%A Montoliu, Raul
%A Perez-Liebana, Diego
%D 2020
%J arXiv:2009.12065
%T Design and Implementation of TAG: A Tabletop Games Framework.
%U https://arxiv.org/abs/2009.12065 - Dockhorn, A., and Lucas, S. (2020)Local Forward Model Learning for GVGAI Games. In IEEE Conference on Computational Intelligence and Games, CIG, pp. 716–723.
@inproceedings{DocLuc2020,
author = {Dockhorn, Alexander and Lucas, Simon},
booktitle = {IEEE Conference on Computational Intelligence and Games, CIG},
keywords = {Model},
month = {08},
pages = {716--723},
title = {Local Forward Model Learning for GVGAI Games},
year = 2020
}%0 Conference Paper
%1 DocLuc2020
%A Dockhorn, Alexander
%A Lucas, Simon
%B IEEE Conference on Computational Intelligence and Games, CIG
%D 2020
%P 716--723
%R 10.1109/CoG47356.2020.9231793
%T Local Forward Model Learning for GVGAI Games
%U https://ieeexplore.ieee.org/document/9231793
%@ 9781728145334 - Eggensperger, K., Haase, K., M{ü}ller, P., Lindauer, M., and Hutter, F. (2020)Neural Model-based Optimization with Right-Censored Observations. In CoRR.
@inproceedings{EggHaa2020,
author = {Eggensperger, Katharina and Haase, Kai and M{ü}ller, Philipp and Lindauer, Marius and Hutter, Frank},
booktitle = {CoRR},
keywords = {Optimization},
month = {09},
title = {Neural Model-based Optimization with Right-Censored Observations},
year = 2020
}%0 Conference Paper
%1 EggHaa2020
%A Eggensperger, Katharina
%A Haase, Kai
%A M{ü}ller, Philipp
%A Lindauer, Marius
%A Hutter, Frank
%B CoRR
%D 2020
%T Neural Model-based Optimization with Right-Censored Observations
%U https://arxiv.org/abs/2009.13828 - Speck, D., Biedenkapp, A., Hutter, F., Mattm{ü}ller, R., and Lindauer, M. (2020)Learning Heuristic Selection with Dynamic Algorithm Configuration. In Proceedings of international workshop on Bridging the Gap Between AI Planning and Reinforcement Learning at ICAPS.
@inproceedings{SpeBie2020,
author = {Speck, David and Biedenkapp, André and Hutter, Frank and Mattm{ü}ller, Robert and Lindauer, Marius},
booktitle = {Proceedings of international workshop on Bridging the Gap Between AI Planning and Reinforcement Learning at ICAPS},
keywords = {Heuristic},
month = {06},
title = {Learning Heuristic Selection with Dynamic Algorithm Configuration},
year = 2020
}%0 Conference Paper
%1 SpeBie2020
%A Speck, David
%A Biedenkapp, André
%A Hutter, Frank
%A Mattm{ü}ller, Robert
%A Lindauer, Marius
%B Proceedings of international workshop on Bridging the Gap Between AI Planning and Reinforcement Learning at ICAPS
%D 2020
%T Learning Heuristic Selection with Dynamic Algorithm Configuration
%U https://arxiv.org/abs/2006.08246 - Reinders, C., and Rosenhahn, B. (2020)Neuronale Netze: Angriffe und Verteidigung - Ich sehe was, was du nicht siehst, iX Developer 2020 – Machine Learning 2.0.
@article{ReiRos2020,
author = {Reinders, Christoph and Rosenhahn, Bodo},
journal = {iX Developer 2020 – Machine Learning 2.0},
keywords = {Netze},
title = {Neuronale Netze: Angriffe und Verteidigung - Ich sehe was, was du nicht siehst},
year = 2020
}%0 Journal Article
%1 ReiRos2020
%A Reinders, Christoph
%A Rosenhahn, Bodo
%D 2020
%J iX Developer 2020 – Machine Learning 2.0
%T Neuronale Netze: Angriffe und Verteidigung - Ich sehe was, was du nicht siehst - S{ü}dbeck, S., Krause, T., and Ostermann, J. (2020)Non-Line-of-Sight Time-Difference-of-Arrival Localization with Explicit Inclusion of Geometry Information in a Simple Diffraction Scenario. In IEEE MMSP 2020 - IEEE International Workshop on Multimedia Signal Processing.
@inproceedings{SueKra2020,
author = {S{ü}dbeck, S{ö}nke and Krause, Thomas and Ostermann, J{ö}rn},
booktitle = {IEEE MMSP 2020 - IEEE International Workshop on Multimedia Signal Processing},
keywords = {Localization},
month = {09},
title = {Non-Line-of-Sight Time-Difference-of-Arrival Localization with Explicit Inclusion of Geometry Information in a Simple Diffraction Scenario},
year = 2020
}%0 Conference Paper
%1 SueKra2020
%A S{ü}dbeck, S{ö}nke
%A Krause, Thomas
%A Ostermann, J{ö}rn
%B IEEE MMSP 2020 - IEEE International Workshop on Multimedia Signal Processing
%D 2020
%T Non-Line-of-Sight Time-Difference-of-Arrival Localization with Explicit Inclusion of Geometry Information in a Simple Diffraction Scenario - Scheffner, I., Gietzelt, M., Abeling, T., Marschollek, M., and Gwinner, W. (2020)Patient Survival After Kidney Transplantation: Important Role of Graft-sustaining Factors as Determined by Predictive Modeling Using Random Survival Forest Analysis, Transplantation 104, 1095–1107.Background: Identification of the relevant factors for death can improve patient's individual risk assessment and decision making. A well-documented patient cohort (n = 892) in a renal transplant program with protocol biopsies was used to establish multivariable models for risk assessment at 3 and 12 months posttransplantation by random survival forest analysis. Methods: Patients transplanted between 2000 and 2007 were observed for up to 11 years. Loss to follow-up was negligible (n = 15). A total of 2251 protocol biopsies and 1214 biopsies for cause were performed. All rejections and clinical borderline rejections in protocol biopsies were treated. Results: Ten-year patient survival was 78%, with inferior survival of patients with graft loss. Using all pre- and posttransplant variables until 3 and 12 months (n = 65), the obtained models showed good performance to predict death (concordance index: 0.77-0.78). Validation with a separate cohort of patients (n = 349) showed a concordance index of 0.76 and good discrimination of risks by the models, despite substantial differences in clinical variables. Random survival forest analysis produced robust models over a wide range of parameter settings. Besides well-established risk factors like age, cardiovascular disease, type 2 diabetes, and graft function, posttransplant urinary tract infection and rejection treatment were important factors. Urinary tract infection and rejection treatment were not specifically associated with death due to infection or malignancy but correlated strongly with inferior graft function and graft loss. Conclusions: The established models indicate the important areas that need special attention in the care of renal transplant patients, particularly modifiable factors like graft rejection and urinary tract infection.
@article{noauthororeditor,
abstract = {Background: Identification of the relevant factors for death can improve patient's individual risk assessment and decision making. A well-documented patient cohort (n = 892) in a renal transplant program with protocol biopsies was used to establish multivariable models for risk assessment at 3 and 12 months posttransplantation by random survival forest analysis. Methods: Patients transplanted between 2000 and 2007 were observed for up to 11 years. Loss to follow-up was negligible (n = 15). A total of 2251 protocol biopsies and 1214 biopsies for cause were performed. All rejections and clinical borderline rejections in protocol biopsies were treated. Results: Ten-year patient survival was 78%, with inferior survival of patients with graft loss. Using all pre- and posttransplant variables until 3 and 12 months (n = 65), the obtained models showed good performance to predict death (concordance index: 0.77-0.78). Validation with a separate cohort of patients (n = 349) showed a concordance index of 0.76 and good discrimination of risks by the models, despite substantial differences in clinical variables. Random survival forest analysis produced robust models over a wide range of parameter settings. Besides well-established risk factors like age, cardiovascular disease, type 2 diabetes, and graft function, posttransplant urinary tract infection and rejection treatment were important factors. Urinary tract infection and rejection treatment were not specifically associated with death due to infection or malignancy but correlated strongly with inferior graft function and graft loss. Conclusions: The established models indicate the important areas that need special attention in the care of renal transplant patients, particularly modifiable factors like graft rejection and urinary tract infection.},
author = {Scheffner, Irina and Gietzelt, Matthias and Abeling, Tanja and Marschollek, Michael and Gwinner, Wilfried},
journal = {Transplantation},
keywords = {l3s},
month = {05},
number = 5,
pages = {1095-1107},
title = {Patient Survival After Kidney Transplantation: Important Role of Graft-sustaining Factors as Determined by Predictive Modeling Using Random Survival Forest Analysis},
volume = 104,
year = 2020
}%0 Journal Article
%1 noauthororeditor
%A Scheffner, Irina
%A Gietzelt, Matthias
%A Abeling, Tanja
%A Marschollek, Michael
%A Gwinner, Wilfried
%D 2020
%J Transplantation
%N 5
%P 1095-1107
%R DOI: 10.1097/TP.0000000000002922
%T Patient Survival After Kidney Transplantation: Important Role of Graft-sustaining Factors as Determined by Predictive Modeling Using Random Survival Forest Analysis
%V 104
%X Background: Identification of the relevant factors for death can improve patient's individual risk assessment and decision making. A well-documented patient cohort (n = 892) in a renal transplant program with protocol biopsies was used to establish multivariable models for risk assessment at 3 and 12 months posttransplantation by random survival forest analysis. Methods: Patients transplanted between 2000 and 2007 were observed for up to 11 years. Loss to follow-up was negligible (n = 15). A total of 2251 protocol biopsies and 1214 biopsies for cause were performed. All rejections and clinical borderline rejections in protocol biopsies were treated. Results: Ten-year patient survival was 78%, with inferior survival of patients with graft loss. Using all pre- and posttransplant variables until 3 and 12 months (n = 65), the obtained models showed good performance to predict death (concordance index: 0.77-0.78). Validation with a separate cohort of patients (n = 349) showed a concordance index of 0.76 and good discrimination of risks by the models, despite substantial differences in clinical variables. Random survival forest analysis produced robust models over a wide range of parameter settings. Besides well-established risk factors like age, cardiovascular disease, type 2 diabetes, and graft function, posttransplant urinary tract infection and rejection treatment were important factors. Urinary tract infection and rejection treatment were not specifically associated with death due to infection or malignancy but correlated strongly with inferior graft function and graft loss. Conclusions: The established models indicate the important areas that need special attention in the care of renal transplant patients, particularly modifiable factors like graft rejection and urinary tract infection. - Liao, W., Cheng, X., Yang, J., Roth, S., Goesele, M., Yang, M. Y., and Rosenhahn, B. (2020)LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery. In XXIV ISPRS Congress, p. 8.
@inproceedings{LiaChe2020,
author = {Liao, Wentong and Cheng, Xiang and Yang, Jingfeng and Roth, Stefan and Goesele, Michael and Yang, Michael Ying and Rosenhahn, Bodo},
booktitle = {XXIV ISPRS Congress},
keywords = {leibnizailab},
month = {09},
note = {oral},
number = 381,
pages = 8,
title = {LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery},
year = 2020
}%0 Conference Paper
%1 LiaChe2020
%A Liao, Wentong
%A Cheng, Xiang
%A Yang, Jingfeng
%A Roth, Stefan
%A Goesele, Michael
%A Yang, Michael Ying
%A Rosenhahn, Bodo
%B XXIV ISPRS Congress
%D 2020
%N 381
%P 8
%T LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery - Dockhorn, A., Saxton, C., and Kruse, R. (2020)Association Rule Mining for Unknown Video Games, Fuzzy Approaches for Soft Computing and Approximate Reasoning: Theories and Applications 257–270.
@article{DocSax2020,
author = {Dockhorn, Alexander and Saxton, Chris and Kruse, Rudolf},
journal = {Fuzzy Approaches for Soft Computing and Approximate Reasoning: Theories and Applications},
keywords = {Mining},
month = 10,
pages = {257--270},
title = {Association Rule Mining for Unknown Video Games},
year = 2020
}%0 Journal Article
%1 DocSax2020
%A Dockhorn, Alexander
%A Saxton, Chris
%A Kruse, Rudolf
%D 2020
%J Fuzzy Approaches for Soft Computing and Approximate Reasoning: Theories and Applications
%P 257--270
%R 10.1007/978-3-030-54341-9_22
%T Association Rule Mining for Unknown Video Games
%U https://link.springer.com/chapter/10.1007/978-3-030-54341-9_22
%@ 978-3-030-54341-9 - Hu, T., Iosifidis, V., Wentong, L., Hang, Z., Yang, M. Y., Ntoutsi, E., and Rosenhahn, B. (2020)FairNN - Conjoint Learning of Fair Representations for Fair Decisions. In 23rd International Conference on Discovery Science.
@inproceedings{HuIos2020,
author = {Hu, Tongxin and Iosifidis, Vasileios and Wentong, Liao and Hang, Zhang and Yang, Michael Ying and Ntoutsi, Eirini and Rosenhahn, Bodo},
booktitle = {23rd International Conference on Discovery Science},
keywords = {for},
month = 10,
note = {Code available: https://github.com/wtliao/FairNN},
title = {FairNN - Conjoint Learning of Fair Representations for Fair Decisions},
year = 2020
}%0 Conference Paper
%1 HuIos2020
%A Hu, Tongxin
%A Iosifidis, Vasileios
%A Wentong, Liao
%A Hang, Zhang
%A Yang, Michael Ying
%A Ntoutsi, Eirini
%A Rosenhahn, Bodo
%B 23rd International Conference on Discovery Science
%D 2020
%T FairNN - Conjoint Learning of Fair Representations for Fair Decisions - Dockhorn, A. (2020)Dissertation: Prediction-based Search for Autonomous Game-Playing, Otto von Guericke University Magdeburg 1–231.
@article{Doc2020a,
author = {Dockhorn, Alexander},
journal = {Otto von Guericke University Magdeburg},
keywords = {Prediction-based},
pages = {1--231},
title = {Dissertation: Prediction-based Search for Autonomous Game-Playing},
year = 2020
}%0 Journal Article
%1 Doc2020a
%A Dockhorn, Alexander
%D 2020
%J Otto von Guericke University Magdeburg
%P 1--231
%R 10.25673/34014
%T Dissertation: Prediction-based Search for Autonomous Game-Playing
%U https://adockhorn.github.io/phdthesis.html - Sen, H., Wentong, L., Rezazadegan Tavakoli, H., Ying Yang, M., Rosenhahn, B., and Pugeault, N. (2020)Image Captioning through Image Transformer. In .
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author = {Sen, He and Wentong, Liao and Rezazadegan Tavakoli, Hamed and Ying Yang, Michael and Rosenhahn, Bodo and Pugeault, Nicolas},
keywords = {l3s},
month = 11,
title = {Image Captioning through Image Transformer},
year = 2020
}%0 Conference Paper
%1 HeSLia2020
%A Sen, He
%A Wentong, Liao
%A Rezazadegan Tavakoli, Hamed
%A Ying Yang, Michael
%A Rosenhahn, Bodo
%A Pugeault, Nicolas
%D 2020
%T Image Captioning through Image Transformer - Samayoa, Y., and Ostermann, J. (2020)Parameter Selection for a Video Communication System based on HEVC and Channel Coding. In IEEE Latin-American Conference on Communications (LATINCOM 2020), p. 5.
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author = {Samayoa, Yasser and Ostermann, J{ö}rn},
booktitle = {IEEE Latin-American Conference on Communications (LATINCOM 2020)},
keywords = {System},
month = 11,
pages = 5,
title = {Parameter Selection for a Video Communication System based on HEVC and Channel Coding},
year = 2020
}%0 Conference Paper
%1 SamOst2020
%A Samayoa, Yasser
%A Ostermann, J{ö}rn
%B IEEE Latin-American Conference on Communications (LATINCOM 2020)
%D 2020
%P 5
%T Parameter Selection for a Video Communication System based on HEVC and Channel Coding - Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F. (2020)Auto-Sklearn 2.0: The Next Generation. In arXiv:2007.04074 [cs.LG].
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booktitle = {arXiv:2007.04074 [cs.LG]},
keywords = {Auto-Sklearn},
month = {07},
title = {Auto-Sklearn 2.0: The Next Generation},
year = 2020
}%0 Conference Paper
%1 FeuEgg2020
%A Feurer, Matthias
%A Eggensperger, Katharina
%A Falkner, Stefan
%A Lindauer, Marius
%A Hutter, Frank
%B arXiv:2007.04074 [cs.LG]
%D 2020
%T Auto-Sklearn 2.0: The Next Generation
%U https://arxiv.org/abs/2007.04074 - Awiszus, M., Schubert, F., and Rosenhahn, B. (2020)TOAD-GAN: Coherent Style Level Generation from a Single Example. In AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Best Student Paper Award.
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author = {Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
booktitle = {AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Best Student Paper Award},
keywords = {TOAD-GAN},
month = 10,
note = {7 pages, 7 figures.},
title = {TOAD-GAN: Coherent Style Level Generation from a Single Example},
year = 2020
}%0 Conference Paper
%1 AwiSch2020
%A Awiszus, Maren
%A Schubert, Frederik
%A Rosenhahn, Bodo
%B AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Best Student Paper Award
%D 2020
%T TOAD-GAN: Coherent Style Level Generation from a Single Example
%U /brokenurl#AAAI, github, arxiv, Award - Henschel, R., von Marcard, T., and Rosenhahn, B. (2020)Accurate Long-Term Multiple People Tracking using Video and Body-Worn IMUs, IEEE Transactions on Image Processing.
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author = {Henschel, Roberto and von Marcard, Timo and Rosenhahn, Bodo},
journal = {IEEE Transactions on Image Processing},
keywords = {Multiple},
title = {Accurate Long-Term Multiple People Tracking using Video and Body-Worn IMUs},
year = 2020
}%0 Journal Article
%1 Henvon2020a
%A Henschel, Roberto
%A von Marcard, Timo
%A Rosenhahn, Bodo
%D 2020
%J IEEE Transactions on Image Processing
%T Accurate Long-Term Multiple People Tracking using Video and Body-Worn IMUs
%U https://ieeexplore.ieee.org/abstract/document/9166762 - Denkena, B., Dittrich, M., Lindauer, M., Mainka, and St{ü}renburg, L. (2020)Using AutoML to Optimize Shape Error Prediction in Milling Processes. In Proceedings of 20th Machining Innovations Conference for Aerospace Industry (MIC).
@inproceedings{DenDit2020a,
author = {Denkena, Berend and Dittrich, Marc and Lindauer, Marius and Mainka and St{ü}renburg, Lukas},
booktitle = {Proceedings of 20th Machining Innovations Conference for Aerospace Industry (MIC)},
keywords = {AutoML},
month = 12,
title = {Using AutoML to Optimize Shape Error Prediction in Milling Processes},
year = 2020
}%0 Conference Paper
%1 DenDit2020a
%A Denkena, Berend
%A Dittrich, Marc
%A Lindauer, Marius
%A Mainka,
%A St{ü}renburg, Lukas
%B Proceedings of 20th Machining Innovations Conference for Aerospace Industry (MIC)
%D 2020
%T Using AutoML to Optimize Shape Error Prediction in Milling Processes
%U https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3724234 - Kuhnke, F., Rumberg, L., and Ostermann, J. (2020)Two-Stream Aural-Visual Affect Analysis in the Wild. In 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pp. 366–371.
@inproceedings{KuhRum2020,
author = {Kuhnke, Felix and Rumberg, Lars and Ostermann, J{ö}rn},
booktitle = {15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)},
keywords = {Affect},
month = {05},
pages = {366-371},
title = {Two-Stream Aural-Visual Affect Analysis in the Wild},
year = 2020
}%0 Conference Paper
%1 KuhRum2020
%A Kuhnke, Felix
%A Rumberg, Lars
%A Ostermann, J{ö}rn
%B 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
%D 2020
%P 366-371
%R 10.1109/FG47880.2020.00056
%T Two-Stream Aural-Visual Affect Analysis in the Wild
%U /brokenurl#code - Awad, N., Shala, G., Deng, D., Mallik, N., Feurer, M., Eggensperger, K., Biedenkapp, A., Vermetten, D., Wang, H., Carola, D., Lindauer, M., and Hutter, F. (2020)Squirrel: A Switching Hyperparameter Optimizer, arxiv.
@article{AwaGre2020a,
author = {Awad, Noor and Shala, Gresa and Deng, Difan and Mallik, Neeratyoy and Feurer, Matthias and Eggensperger, Katharina and Biedenkapp, André and Vermetten, Diederick and Wang, Hao and Carola, Doerr and Lindauer, Marius and Hutter, Frank},
journal = {arxiv},
keywords = {Hyperparameter},
month = 12,
title = {Squirrel: A Switching Hyperparameter Optimizer},
year = 2020
}%0 Journal Article
%1 AwaGre2020a
%A Awad, Noor
%A Shala, Gresa
%A Deng, Difan
%A Mallik, Neeratyoy
%A Feurer, Matthias
%A Eggensperger, Katharina
%A Biedenkapp, André
%A Vermetten, Diederick
%A Wang, Hao
%A Carola, Doerr
%A Lindauer, Marius
%A Hutter, Frank
%D 2020
%J arxiv
%T Squirrel: A Switching Hyperparameter Optimizer
%U https://arxiv.org/abs/2012.08180 - Tan, D., Maybery, M., Gilani, S. Z., Alvares, G., Mian, A., Suter, D., and Whitehouse, A. (2020)A broad autism phenotype expressed in facial morphology, Translational Psychiatry 10.
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author = {Tan, Diana and Maybery, Murray and Gilani, Syed Zulqarnain and Alvares, Gail and Mian, Ajmal and Suter, David and Whitehouse, Andrew},
journal = {Translational Psychiatry},
keywords = {leibnizailab},
number = 1,
title = {A broad autism phenotype expressed in facial morphology},
volume = 10,
year = 2020
}%0 Journal Article
%1 TanPsych2019
%A Tan, Diana
%A Maybery, Murray
%A Gilani, Syed Zulqarnain
%A Alvares, Gail
%A Mian, Ajmal
%A Suter, David
%A Whitehouse, Andrew
%D 2020
%J Translational Psychiatry
%N 1
%R 10.1038/s41398-020-0695-z
%T A broad autism phenotype expressed in facial morphology
%V 10 - Pestel-Schiller, U., and Ostermann, J. (2020)Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability. In 13th European Conference on Synthetic Aperture Radar.
@inproceedings{PesOst2020,
author = {Pestel-Schiller, Ulrike and Ostermann, J{ö}rn},
booktitle = {13th European Conference on Synthetic Aperture Radar},
keywords = {HEVC},
month = {06},
title = {Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability},
year = 2020
}%0 Conference Paper
%1 PesOst2020
%A Pestel-Schiller, Ulrike
%A Ostermann, J{ö}rn
%B 13th European Conference on Synthetic Aperture Radar
%D 2020
%T Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability - Minh, C. N. D., Gilani, S. Z., Islam, S., and Suter, D. (2020)Learning Affordance Segmentation: An Investigative Study. In DICTA2020.
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author = {Minh, Chau Nguyen Duc and Gilani, Syed Zulqarnain and Islam, Syed and Suter, David},
booktitle = {DICTA2020},
keywords = {leibnizailab},
title = {Learning Affordance Segmentation: An Investigative Study},
year = 2020
}%0 Conference Paper
%1 chauDICTA2020
%A Minh, Chau Nguyen Duc
%A Gilani, Syed Zulqarnain
%A Islam, Syed
%A Suter, David
%B DICTA2020
%D 2020
%R 10.1109/DICTA51227.2020.9363390
%T Learning Affordance Segmentation: An Investigative Study - Dockhorn, A., Grueso, J. H., Jeurissen, D., and Perez-Liebana, D. (2020)“Stratega”: A General Strategy Games Framework. In Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); Artificial Intelligence for Strategy Games, pp. 1–7.
@inproceedings{DocGru2020,
author = {Dockhorn, Alexander and Grueso, Jorge Hurtado and Jeurissen, Dominik and Perez-Liebana, Diego},
booktitle = {Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); Artificial Intelligence for Strategy Games},
keywords = {“Stratega”},
pages = {1--7},
title = {“Stratega”: A General Strategy Games Framework},
year = 2020
}%0 Conference Paper
%1 DocGru2020
%A Dockhorn, Alexander
%A Grueso, Jorge Hurtado
%A Jeurissen, Dominik
%A Perez-Liebana, Diego
%B Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); Artificial Intelligence for Strategy Games
%D 2020
%P 1--7
%T “Stratega”: A General Strategy Games Framework
%U http://ceur-ws.org/Vol-2862/ - Biedenkapp, A., Rajan, R., Hutter, F., and Lindauer, M. (2020)Towards TempoRL: Learning When to Act. In Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML’20).
@inproceedings{BieRaj2020,
author = {Biedenkapp, Andre and Rajan, Raghu and Hutter, Frank and Lindauer, Marius},
booktitle = {Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20)},
keywords = {TempoRL},
month = {07},
title = {Towards TempoRL: Learning When to Act},
year = 2020
}%0 Conference Paper
%1 BieRaj2020
%A Biedenkapp, Andre
%A Rajan, Raghu
%A Hutter, Frank
%A Lindauer, Marius
%B Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20)
%D 2020
%T Towards TempoRL: Learning When to Act - Dockhorn, A. (2020)Vorhersagebasierte Suche f{ü}r autonomes Spielen, pp. 69–78, GI.
@book{Doc2020,
author = {Dockhorn, Alexander},
keywords = {Vorhersagebasierte},
pages = {69-78},
publisher = {GI},
title = {Vorhersagebasierte Suche f{ü}r autonomes Spielen},
year = 2020
}%0 Book
%1 Doc2020
%A Dockhorn, Alexander
%D 2020
%I GI
%P 69-78
%R 20.500.12116/37928
%T Vorhersagebasierte Suche f{ü}r autonomes Spielen
%U https://dl.gi.de/20.500.12116/37928
%@ 978-3-88579-775-3 - Wulff, A., Mast, M., Hassler, M., Montag, S., Marschollek, M., and Jack, T. (2020)Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing, Methods Inf Med 59, e64-e78.BACKGROUND: Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly. OBJECTIVES: The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories. METHODS: We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School. RESULTS: We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall. CONCLUSION: The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.
@article{wulff2020designing,
abstract = {BACKGROUND: Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly. OBJECTIVES: The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories. METHODS: We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School. RESULTS: We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall. CONCLUSION: The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.},
author = {Wulff, A. and Mast, M. and Hassler, M. and Montag, S. and Marschollek, M. and Jack, T.},
journal = {Methods Inf Med},
keywords = {l3s},
month = 12,
number = {S 02},
pages = {e64-e78},
title = {Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing},
volume = 59,
year = 2020
}%0 Journal Article
%1 wulff2020designing
%A Wulff, A.
%A Mast, M.
%A Hassler, M.
%A Montag, S.
%A Marschollek, M.
%A Jack, T.
%D 2020
%J Methods Inf Med
%N S 02
%P e64-e78
%R doi: 10.1055/s-0040-1716403
%T Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing
%U https://www.ncbi.nlm.nih.gov/pubmed/33058101
%V 59
%X BACKGROUND: Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly. OBJECTIVES: The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories. METHODS: We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School. RESULTS: We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall. CONCLUSION: The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission. - Dockhorn, A., and Kruse, R. (2020)Forward Model Learning for Motion Control Tasks. In 2020 IEEE 10th International Conference on Intelligent Systems (IS), pp. 1–5.
@inproceedings{DocKru2020b,
author = {Dockhorn, Alexander and Kruse, Rudolf},
booktitle = {2020 IEEE 10th International Conference on Intelligent Systems (IS)},
keywords = {Model},
month = {09},
pages = {1--5},
title = {Forward Model Learning for Motion Control Tasks},
year = 2020
}%0 Conference Paper
%1 DocKru2020b
%A Dockhorn, Alexander
%A Kruse, Rudolf
%B 2020 IEEE 10th International Conference on Intelligent Systems (IS)
%D 2020
%P 1--5
%R 10.1109/IS48319.2020.9199978
%T Forward Model Learning for Motion Control Tasks
%U https://ieeexplore.ieee.org/document/9199978
%@ 9781728154565 - Gra{{\"s}}hof, S., Ackermann, H., Brandt, S., and Ostermann, J. (2020)Multilinear Modelling of Faces and Expressions, Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
@article{GraAck2020,
author = {Gra{{\"s}}hof, Stella and Ackermann, Hanno and Brandt, Sami and Ostermann, J{ö}rn},
journal = {Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
keywords = {Faces},
month = {04},
note = {early access},
title = {Multilinear Modelling of Faces and Expressions},
year = 2020
}%0 Journal Article
%1 GraAck2020
%A Gra{{\"s}}hof, Stella
%A Ackermann, Hanno
%A Brandt, Sami
%A Ostermann, J{ö}rn
%D 2020
%J Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
%T Multilinear Modelling of Faces and Expressions
%U https://ieeexplore.ieee.org/document/9067086 - J{ü}rgens, H., Hinrichs, R., and Ostermann, J. (2020)Recognizing Guitar Effects and Their Parameter Settings. In Proceedings of the DAFx2020 (Vol I).
@inproceedings{JueHin2020,
author = {J{ü}rgens, Henrik and Hinrichs, Reemt and Ostermann, J{ö}rn},
booktitle = {Proceedings of the DAFx2020 (Vol I)},
keywords = {Settings},
title = {Recognizing Guitar Effects and Their Parameter Settings},
year = 2020
}%0 Conference Paper
%1 JueHin2020
%A J{ü}rgens, Henrik
%A Hinrichs, Reemt
%A Ostermann, J{ö}rn
%B Proceedings of the DAFx2020 (Vol I)
%D 2020
%T Recognizing Guitar Effects and Their Parameter Settings - Souza, A., Nardi, L., Oliveira, L., Olukotun, K., Lindauer, M., and Hutter, F. (2020)Prior-guided Bayesian Optimization. In arxiv:2006.14608[cs.LG].
@inproceedings{SouNar2020,
author = {Souza, Artur and Nardi, Luigi and Oliveira, Leonardo and Olukotun, Kunle and Lindauer, Marius and Hutter, Frank},
booktitle = {arxiv:2006.14608[cs.LG]},
keywords = {Optimization},
month = {06},
title = {Prior-guided Bayesian Optimization},
year = 2020
}%0 Conference Paper
%1 SouNar2020
%A Souza, Artur
%A Nardi, Luigi
%A Oliveira, Leonardo
%A Olukotun, Kunle
%A Lindauer, Marius
%A Hutter, Frank
%B arxiv:2006.14608[cs.LG]
%D 2020
%T Prior-guided Bayesian Optimization
%U https://arxiv.org/abs/2006.14608
2019
- Dengel, R., Woiwode, D., Florsch{ü}tz, N., Huber, V., Muller, T., von Pichowski, J., Rabinowitsch, A., Scholz, S., Sch{ü}lein, H., Steinweg, E., Stippel, B., St{ö}ferle, P., Wittekind, I., Wizemann, O., Zaft, A., Zembrot, L., and Griebenow, K. (2019)QUEST ON BEXUS 27. In 24th ESA Symposium on European Rocket \& Balloon Programmes and Related.
@inproceedings{DenWoi2019,
author = {Dengel, Ric and Woiwode, Dominik and Florsch{ü}tz, Nico and Huber, Valentin and Muller, Tim and von Pichowski, Jan and Rabinowitsch, Alexander and Scholz, Sebastian and Sch{ü}lein, Hans and Steinweg, Eike and Stippel, Benjamin and St{ö}ferle, Peter and Wittekind, Isabell and Wizemann, Oliver and Zaft, Alexander and Zembrot, Lukas and Griebenow, Katrin},
booktitle = {24th ESA Symposium on European Rocket \& Balloon Programmes and Related},
keywords = {ON},
month = 10,
title = {QUEST ON BEXUS 27},
year = 2019
}%0 Conference Paper
%1 DenWoi2019
%A Dengel, Ric
%A Woiwode, Dominik
%A Florsch{ü}tz, Nico
%A Huber, Valentin
%A Muller, Tim
%A von Pichowski, Jan
%A Rabinowitsch, Alexander
%A Scholz, Sebastian
%A Sch{ü}lein, Hans
%A Steinweg, Eike
%A Stippel, Benjamin
%A St{ö}ferle, Peter
%A Wittekind, Isabell
%A Wizemann, Oliver
%A Zaft, Alexander
%A Zembrot, Lukas
%A Griebenow, Katrin
%B 24th ESA Symposium on European Rocket \& Balloon Programmes and Related
%D 2019
%T QUEST ON BEXUS 27 - Dockhorn, A., and Mostaghim, S. (2019)Introducing the Hearthstone-AI Competition, arXiv:1906.04238 1–4.
@article{DocMos2019,
author = {Dockhorn, Alexander and Mostaghim, Sanaz},
journal = {arXiv:1906.04238},
keywords = {Hearthstone-AI},
month = {05},
pages = {1--4},
title = {Introducing the Hearthstone-AI Competition},
year = 2019
}%0 Journal Article
%1 DocMos2019
%A Dockhorn, Alexander
%A Mostaghim, Sanaz
%D 2019
%J arXiv:1906.04238
%P 1--4
%T Introducing the Hearthstone-AI Competition
%U http://arxiv.org/abs/1906.04238 - Dockhorn, A., Lucas, S. M., Volz, V., Bravi, I., Gaina, R. D., and Perez-Liebana, D. (2019)Learning Local Forward Models on Unforgiving Games. In 2019 IEEE Conference on Games (CoG).
@inproceedings{DocLuc2019,
author = {Dockhorn, Alexander and Lucas, Simon M and Volz, Vanessa and Bravi, Ivan and Gaina, Raluca D and Perez-Liebana, Diego},
booktitle = {2019 IEEE Conference on Games (CoG)},
keywords = {Models},
month = {08},
title = {Learning Local Forward Models on Unforgiving Games},
year = 2019
}%0 Conference Paper
%1 DocLuc2019
%A Dockhorn, Alexander
%A Lucas, Simon M
%A Volz, Vanessa
%A Bravi, Ivan
%A Gaina, Raluca D
%A Perez-Liebana, Diego
%B 2019 IEEE Conference on Games (CoG)
%D 2019
%R 10.1109/CIG.2019.8848044
%T Learning Local Forward Models on Unforgiving Games
%U https://ieeexplore.ieee.org/document/8848044/
%@ 978-1-7281-1884-0 - Dockhorn, A., Schwensfeier, T., and Kruse, R. (2019)Fuzzy Multiset Clustering for Metagame Analysis. In Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), pp. 536–543.
@inproceedings{DocSch2019a,
author = {Dockhorn, Alexander and Schwensfeier, Tony and Kruse, Rudolf},
booktitle = {Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)},
keywords = {Multiset},
month = {08},
pages = {536-543},
title = {Fuzzy Multiset Clustering for Metagame Analysis},
year = 2019
}%0 Conference Paper
%1 DocSch2019a
%A Dockhorn, Alexander
%A Schwensfeier, Tony
%A Kruse, Rudolf
%B Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
%D 2019
%P 536-543
%R 10.2991/eusflat-19.2019.74
%T Fuzzy Multiset Clustering for Metagame Analysis
%U https://www.atlantis-press.com/proceedings/eusflat-19/125914844
%@ 978-94-6252-770-6 - Lucas, S. M., Alexander, Dockhorn, V., Gaina, R. D., Bravi, I., Perez-Liebana, D., Mostaghim, S., and Kruse, R. (2019)A Local Approach to Forward Model Learning: Results on the Game of Life Game. In 2019 IEEE Conference on Games (CoG), pp. 1–8.
@inproceedings{LucDoc2019,
author = {Lucas, Simon M and Alexander and Dockhorn, Volz and Gaina, Raluca D and Bravi, Ivan and Perez-Liebana, Diego and Mostaghim, Sanaz and Kruse, Rudolf},
booktitle = {2019 IEEE Conference on Games (CoG)},
keywords = {Local},
month = {08},
pages = {1--8},
title = {A Local Approach to Forward Model Learning: Results on the Game of Life Game},
year = 2019
}%0 Conference Paper
%1 LucDoc2019
%A Lucas, Simon M
%A Alexander,
%A Dockhorn, Volz
%A Gaina, Raluca D
%A Bravi, Ivan
%A Perez-Liebana, Diego
%A Mostaghim, Sanaz
%A Kruse, Rudolf
%B 2019 IEEE Conference on Games (CoG)
%D 2019
%P 1--8
%R 10.1109/CIG.2019.8848002
%T A Local Approach to Forward Model Learning: Results on the Game of Life Game
%U https://ieeexplore.ieee.org/document/8848002/
%@ 978-1-7281-1884-0 - Wilbers, D., Rumberg, L., and Stachniss, C. (2019)Approximating marginalization with sparse global priors for sliding window SLAM-graphs.. In 2019 Third IEEE International Conference on Robotic Computing (IRC), pp. 25–31.
@inproceedings{WilRum2019,
author = {Wilbers, Daniel and Rumberg, Lars and Stachniss, Cyrill},
booktitle = {2019 Third IEEE International Conference on Robotic Computing (IRC)},
keywords = {Approximating},
pages = {25--31},
title = {Approximating marginalization with sparse global priors for sliding window SLAM-graphs.},
year = 2019
}%0 Conference Paper
%1 WilRum2019
%A Wilbers, Daniel
%A Rumberg, Lars
%A Stachniss, Cyrill
%B 2019 Third IEEE International Conference on Robotic Computing (IRC)
%D 2019
%P 25--31
%T Approximating marginalization with sparse global priors for sliding window SLAM-graphs. - Ostermann, J., Denkena, B., Bergmann, B., Schmidt, A., Krause, T., and Voges, J. (2019)Compression of Machine Tool Data, ISO/IEC JTC1/SC29/WG11.
@article{OstDen2019,
author = {Ostermann, J{ö}rn and Denkena, Berend and Bergmann, Benjamin and Schmidt, Alexander and Krause, Thomas and Voges, Jan},
journal = {ISO/IEC JTC1/SC29/WG11},
keywords = {Machine},
month = {07},
title = {Compression of Machine Tool Data},
year = 2019
}%0 Journal Article
%1 OstDen2019
%A Ostermann, J{ö}rn
%A Denkena, Berend
%A Bergmann, Benjamin
%A Schmidt, Alexander
%A Krause, Thomas
%A Voges, Jan
%D 2019
%J ISO/IEC JTC1/SC29/WG11
%T Compression of Machine Tool Data
2018
- Dockhorn, A., and Apeldoorn, D. (2018)Forward Model Approximation for General Video Game Learning. In Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games (CIG’18), pp. 425–432.
@inproceedings{DocApe2018,
author = {Dockhorn, Alexander and Apeldoorn, Daan},
booktitle = {Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games (CIG’18)},
keywords = {Model},
month = {08},
pages = {425–432},
title = {Forward Model Approximation for General Video Game Learning},
year = 2018
}%0 Conference Paper
%1 DocApe2018
%A Dockhorn, Alexander
%A Apeldoorn, Daan
%B Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games (CIG’18)
%D 2018
%P 425–432
%R 10.1109/CIG.2018.8490411
%T Forward Model Approximation for General Video Game Learning
%U https://ieeexplore.ieee.org/document/8490411/
%@ 9781538643594 - Dockhorn, A., and Kruse, R. (2018)Detecting Sensor Dependencies for Building Complementary Model Ensembles. In Proceedings of the 28. Workshop Computational Intelligence, Dortmund, 29.-30. November 2018, pp. 217–234.
@inproceedings{DocKru2018,
author = {Dockhorn, Alexander and Kruse, Rudolf},
booktitle = {Proceedings of the 28. Workshop Computational Intelligence, Dortmund, 29.-30. November 2018},
keywords = {Sensor},
pages = {217--234},
title = {Detecting Sensor Dependencies for Building Complementary Model Ensembles},
year = 2018
}%0 Conference Paper
%1 DocKru2018
%A Dockhorn, Alexander
%A Kruse, Rudolf
%B Proceedings of the 28. Workshop Computational Intelligence, Dortmund, 29.-30. November 2018
%D 2018
%P 217--234
%T Detecting Sensor Dependencies for Building Complementary Model Ensembles
%U https://publikationen.bibliothek.kit.edu/1000085935
%@ 978-3-7315-0845-8 - Sabsch, T., Braune, C., Dockhorn, A., and Kruse, R. (2018)Using a multiobjective genetic algorithm for curve approximation. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, pp. 1–6.
@inproceedings{SabBra2018,
author = {Sabsch, Tim and Braune, Christian and Dockhorn, Alexander and Kruse, Rudolf},
booktitle = {2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings},
keywords = {algorithm},
month = {01},
pages = {1--6},
title = {Using a multiobjective genetic algorithm for curve approximation},
year = 2018
}%0 Conference Paper
%1 SabBra2018
%A Sabsch, Tim
%A Braune, Christian
%A Dockhorn, Alexander
%A Kruse, Rudolf
%B 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
%D 2018
%P 1--6
%R 10.1109/SSCI.2017.8285179
%T Using a multiobjective genetic algorithm for curve approximation
%U https://ieeexplore.ieee.org/document/8285179
%@ 9781538627259 - Dockhorn, A., Frick, M., Akkaya, {Ü}nal, and Kruse, R. (2018)Predicting Opponent Moves for Improving Hearthstone AI. In 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018, pp. 621–632.
@inproceedings{DocFri2018,
author = {Dockhorn, Alexander and Frick, Max and Akkaya, {Ü}nal and Kruse, Rudolf},
booktitle = {17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018},
keywords = {Opponent},
pages = {621--632},
title = {Predicting Opponent Moves for Improving Hearthstone AI},
year = 2018
}%0 Conference Paper
%1 DocFri2018
%A Dockhorn, Alexander
%A Frick, Max
%A Akkaya, {Ü}nal
%A Kruse, Rudolf
%B 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018
%D 2018
%P 621--632
%R 10.1007/978-3-319-91476-3_51
%T Predicting Opponent Moves for Improving Hearthstone AI
%U http://link.springer.com/10.1007/978-3-319-91476-3_51 - Waltermann, C., Bethmann, K., Doering, A., Jjang, Y., Baumann, A. L., Anglemahr, M., and Schade, W. (2018)Multiple off-axis fiber Bragg gratings for 3D shape sensing, Applied Optics.
@article{WalBet2018,
author = {Waltermann, C. and Bethmann, K. and Doering, A. and Jjang, Y. and Baumann, A. L. and Anglemahr, M. and Schade, W.},
journal = {Applied Optics},
keywords = {Bragg},
title = {Multiple off-axis fiber Bragg gratings for 3D shape sensing},
year = 2018
}%0 Journal Article
%1 WalBet2018
%A Waltermann, C.
%A Bethmann, K.
%A Doering, A.
%A Jjang, Y.
%A Baumann, A. L.
%A Anglemahr, M.
%A Schade, W.
%D 2018
%J Applied Optics
%T Multiple off-axis fiber Bragg gratings for 3D shape sensing - Pichler, E., Bethmann, K., Kelb, C., and Schade, W. (2018)Rapid prototyping of all-polymer AWGs for FBG readout using direct laser lithography, Optics Letters.
@article{PicBet2018,
author = {Pichler, E. and Bethmann, K. and Kelb, C. and Schade, W.},
journal = {Optics Letters},
keywords = {AWGs},
month = {06},
title = {Rapid prototyping of all-polymer AWGs for FBG readout using direct laser lithography},
year = 2018
}%0 Journal Article
%1 PicBet2018
%A Pichler, E.
%A Bethmann, K.
%A Kelb, C.
%A Schade, W.
%D 2018
%J Optics Letters
%T Rapid prototyping of all-polymer AWGs for FBG readout using direct laser lithography - Dockhorn, A., Tippelt, T., and Kruse, R. (2018)Model Decomposition for Forward Model Approximation. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1751–1757.
@inproceedings{DocTip2018,
author = {Dockhorn, Alexander and Tippelt, Tim and Kruse, Rudolf},
booktitle = {2018 IEEE Symposium Series on Computational Intelligence (SSCI)},
keywords = {Decomposition},
month = 11,
pages = {1751--1757},
title = {Model Decomposition for Forward Model Approximation},
year = 2018
}%0 Conference Paper
%1 DocTip2018
%A Dockhorn, Alexander
%A Tippelt, Tim
%A Kruse, Rudolf
%B 2018 IEEE Symposium Series on Computational Intelligence (SSCI)
%D 2018
%P 1751--1757
%R 10.1109/SSCI.2018.8628624
%T Model Decomposition for Forward Model Approximation
%U https://ieeexplore.ieee.org/document/8628624/
%@ 978-1-5386-9276-9
2017
- Dockhorn, A., Doell, C., Hewelt, M., and Kruse, R. (2017)A decision heuristic for Monte Carlo tree search doppelkopf agents. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8.
@inproceedings{DocDoe2017,
author = {Dockhorn, Alexander and Doell, Christoph and Hewelt, Matthias and Kruse, Rudolf},
booktitle = {2017 IEEE Symposium Series on Computational Intelligence (SSCI)},
keywords = {Monte},
month = 11,
pages = {1--8},
title = {A decision heuristic for Monte Carlo tree search doppelkopf agents},
year = 2017
}%0 Conference Paper
%1 DocDoe2017
%A Dockhorn, Alexander
%A Doell, Christoph
%A Hewelt, Matthias
%A Kruse, Rudolf
%B 2017 IEEE Symposium Series on Computational Intelligence (SSCI)
%D 2017
%P 1--8
%R 10.1109/SSCI.2017.8285181
%T A decision heuristic for Monte Carlo tree search doppelkopf agents
%U http://ieeexplore.ieee.org/document/8285181/
%@ 978-1-5386-2726-6 - Dockhorn, A., and Kruse, R. (2017)Combining cooperative and adversarial coevolution in the context of pac-man. In 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017, pp. 60–67.
@inproceedings{DocKru2017a,
author = {Dockhorn, Alexander and Kruse, Rudolf},
booktitle = {2017 IEEE Conference on Computational Intelligence and Games, CIG 2017},
keywords = {cooperative},
pages = {60--67},
title = {Combining cooperative and adversarial coevolution in the context of pac-man},
year = 2017
}%0 Conference Paper
%1 DocKru2017a
%A Dockhorn, Alexander
%A Kruse, Rudolf
%B 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017
%D 2017
%P 60--67
%R 10.1109/CIG.2017.8080416
%T Combining cooperative and adversarial coevolution in the context of pac-man
%U https://ieeexplore.ieee.org/document/8080416
%@ 9781538632338
2016
- Orighici, R., Bethmann, K., Zywietz, U., Reinhard, C., and Schade, W. (2016)All-polymer arrayed waveguide gratings at 850 nm: design, fabrication and characterization, Optics Letters.
@article{OriBet2016,
author = {Orighici, R. and Bethmann, K. and Zywietz, U. and Reinhard, C. and Schade, W.},
journal = {Optics Letters},
keywords = {waveguide},
title = {All-polymer arrayed waveguide gratings at 850 nm: design, fabrication and characterization},
year = 2016
}%0 Journal Article
%1 OriBet2016
%A Orighici, R.
%A Bethmann, K.
%A Zywietz, U.
%A Reinhard, C.
%A Schade, W.
%D 2016
%J Optics Letters
%T All-polymer arrayed waveguide gratings at 850 nm: design, fabrication and characterization - Dockhorn, A., Braune, C., and Kruse, R. (2016)Variable density based clustering. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8.
@inproceedings{DocBra2016,
author = {Dockhorn, Alexander and Braune, Christian and Kruse, Rudolf},
booktitle = {2016 IEEE Symposium Series on Computational Intelligence (SSCI)},
keywords = {density},
month = 12,
pages = {1--8},
title = {Variable density based clustering},
year = 2016
}%0 Conference Paper
%1 DocBra2016
%A Dockhorn, Alexander
%A Braune, Christian
%A Kruse, Rudolf
%B 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
%D 2016
%P 1--8
%R 10.1109/SSCI.2016.7849925
%T Variable density based clustering
%U http://ieeexplore.ieee.org/document/7849925/
%@ 978-1-5090-4240-1
2015
- Waltermann, C., Baumann, A. L., Bethmann, K., Doering, A., Koch, J., Angelmahr, m., and Schade, W. (2015)Femtosecond laser processing of evanescence field coupled waveguides in single mode glass fibers for optical 3D shape sensing and navigation. In Fiber Optic Sensors and Applications.
@inproceedings{WalBau2015,
author = {Waltermann, C. and Baumann, A. L. and Bethmann, K. and Doering, A. and Koch, J. and m. Angelmahr and Schade, W.},
booktitle = {Fiber Optic Sensors and Applications},
keywords = {processing},
title = {Femtosecond laser processing of evanescence field coupled waveguides in single mode glass fibers for optical 3D shape sensing and navigation},
year = 2015
}%0 Conference Paper
%1 WalBau2015
%A Waltermann, C.
%A Baumann, A. L.
%A Bethmann, K.
%A Doering, A.
%A Koch, J.
%A Angelmahr, m.
%A Schade, W.
%B Fiber Optic Sensors and Applications
%D 2015
%T Femtosecond laser processing of evanescence field coupled waveguides in single mode glass fibers for optical 3D shape sensing and navigation - Pichler, E., Bethmann, K., Zywietz, U., Reinhard, C., Spad, C., Gleissner, U., Kelb, C., Roth, B., Willer, U., and Schade, W. (2015)Ring resonators in polymer foils for sensing of gaseous species. In Fiber Optic Sensors and Applications.
@inproceedings{PicBet2015,
author = {Pichler, E. and Bethmann, K. and Zywietz, U. and Reinhard, C. and Spad, C. and Gleissner, U. and Kelb, C. and Roth, B. and Willer, U. and Schade, W.},
booktitle = {Fiber Optic Sensors and Applications},
keywords = {resonators},
title = {Ring resonators in polymer foils for sensing of gaseous species},
year = 2015
}%0 Conference Paper
%1 PicBet2015
%A Pichler, E.
%A Bethmann, K.
%A Zywietz, U.
%A Reinhard, C.
%A Spad, C.
%A Gleissner, U.
%A Kelb, C.
%A Roth, B.
%A Willer, U.
%A Schade, W.
%B Fiber Optic Sensors and Applications
%D 2015
%T Ring resonators in polymer foils for sensing of gaseous species - Held, P., Dockhorn, A., Krause, B., and Kruse, R. (2015)Clustering Social Networks Using Competing Ant Hives. In 2015 Second European Network Intelligence Conference, pp. 67–74.
@inproceedings{HelDoc2015,
author = {Held, Pascal and Dockhorn, Alexander and Krause, Benjamin and Kruse, Rudolf},
booktitle = {2015 Second European Network Intelligence Conference},
keywords = {Clustering},
month = {09},
pages = {67--74},
title = {Clustering Social Networks Using Competing Ant Hives},
year = 2015
}%0 Conference Paper
%1 HelDoc2015
%A Held, Pascal
%A Dockhorn, Alexander
%A Krause, Benjamin
%A Kruse, Rudolf
%B 2015 Second European Network Intelligence Conference
%D 2015
%P 67--74
%R 10.1109/ENIC.2015.18
%T Clustering Social Networks Using Competing Ant Hives
%U http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7321238
%@ 978-1-4673-7592-4 - Bethmann, K., Orghici, R., Pichler, E., Zywietz, U., Reinhard, C., Schmidt, T., Gleissner, U., Kelb, C., Roth, B., Willer, U., and Schade, W. (2015)New design for a wavelength demultiplexing device. In Fiber Optic Sensors and Applications.
@inproceedings{BetOrg2015,
author = {Bethmann, K. and Orghici, R. and Pichler, E. and Zywietz, U. and Reinhard, C. and Schmidt, T. and Gleissner, U. and Kelb, C. and Roth, B. and Willer, U. and Schade, W.},
booktitle = {Fiber Optic Sensors and Applications},
keywords = {device},
month = {03},
title = {New design for a wavelength demultiplexing device},
year = 2015
}%0 Conference Paper
%1 BetOrg2015
%A Bethmann, K.
%A Orghici, R.
%A Pichler, E.
%A Zywietz, U.
%A Reinhard, C.
%A Schmidt, T.
%A Gleissner, U.
%A Kelb, C.
%A Roth, B.
%A Willer, U.
%A Schade, W.
%B Fiber Optic Sensors and Applications
%D 2015
%T New design for a wavelength demultiplexing device - Dockhorn, A., Braune, C., and Kruse, R. (2015)An Alternating Optimization Approach based on Hierarchical Adaptations of DBSCAN. In 2015 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 749–755.
@inproceedings{DocBra2015,
author = {Dockhorn, Alexander and Braune, Christian and Kruse, Rudolf},
booktitle = {2015 IEEE Symposium Series on Computational Intelligence (SSCI)},
keywords = {Optimization},
number = 2,
pages = {749--755},
title = {An Alternating Optimization Approach based on Hierarchical Adaptations of DBSCAN},
year = 2015
}%0 Conference Paper
%1 DocBra2015
%A Dockhorn, Alexander
%A Braune, Christian
%A Kruse, Rudolf
%B 2015 IEEE Symposium Series on Computational Intelligence (SSCI)
%D 2015
%N 2
%P 749--755
%R 10.1109/SSCI.2015.113
%T An Alternating Optimization Approach based on Hierarchical Adaptations of DBSCAN
%U https://ieeexplore.ieee.org/document/7376687
%@ 9781479975600 - Dockhorn, A. (2015)Master Thesis: Hierarchical Extensions and Cluster Validation Techniques for DBSCAN, Otto von Guericke University Magdeburg 1–80.
@article{Doc2015a,
author = {Dockhorn, Alexander},
journal = {Otto von Guericke University Magdeburg},
keywords = {Extensions},
pages = {1-80},
title = {Master Thesis: Hierarchical Extensions and Cluster Validation Techniques for DBSCAN},
year = 2015
}%0 Journal Article
%1 Doc2015a
%A Dockhorn, Alexander
%D 2015
%J Otto von Guericke University Magdeburg
%P 1-80
%T Master Thesis: Hierarchical Extensions and Cluster Validation Techniques for DBSCAN - Held, P., Dockhorn, A., and Kruse, R. (2015)On Merging and Dividing Social Graphs, Journal of Artificial Intelligence and Soft Computing Research 5, 23–49.
@article{HelDoc2015a,
author = {Held, Pascal and Dockhorn, Alexander and Kruse, Rudolf},
journal = {Journal of Artificial Intelligence and Soft Computing Research},
keywords = {On},
month = {01},
number = 1,
pages = {23--49},
title = {On Merging and Dividing Social Graphs},
volume = 5,
year = 2015
}%0 Journal Article
%1 HelDoc2015a
%A Held, Pascal
%A Dockhorn, Alexander
%A Kruse, Rudolf
%D 2015
%J Journal of Artificial Intelligence and Soft Computing Research
%N 1
%P 23--49
%R 10.1515/jaiscr-2015-0017
%T On Merging and Dividing Social Graphs
%U http://content.sciendo.com/view/journals/jaiscr/5/1/article-p23.xml
%V 5
2014
- Dockhorn, A. (2014)Bachelor Thesis: Computergest{ü}tzte Analyse onkologischer Daten mithilfe Graphischer Modelle, Otto von Guericke University of Magdeburg 1–80.
@article{Doc2014,
author = {Dockhorn, Alexander},
journal = {Otto von Guericke University of Magdeburg},
keywords = {Bachelor},
month = {04},
pages = {1--80},
title = {Bachelor Thesis: Computergest{ü}tzte Analyse onkologischer Daten mithilfe Graphischer Modelle},
year = 2014
}%0 Journal Article
%1 Doc2014
%A Dockhorn, Alexander
%D 2014
%J Otto von Guericke University of Magdeburg
%P 1--80
%T Bachelor Thesis: Computergest{ü}tzte Analyse onkologischer Daten mithilfe Graphischer Modelle - Held, P., Dockhorn, A., and Kruse, R. (2014)On Merging and Dividing of Barabasi-Albert-graphs. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS).
@inproceedings{HelDoc2014,
author = {Held, Pascal and Dockhorn, Alexander and Kruse, Rudolf},
booktitle = {2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)},
keywords = {On},
title = {On Merging and Dividing of Barabasi-Albert-graphs},
volume = 444,
year = 2014
}%0 Conference Paper
%1 HelDoc2014
%A Held, Pascal
%A Dockhorn, Alexander
%A Kruse, Rudolf
%B 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)
%D 2014
%R 10.1109/EALS.2014.7009499
%T On Merging and Dividing of Barabasi-Albert-graphs
%U https://ieeexplore.ieee.org/document/7009499
%V 444
%@ 978-1-4799-4494-1 - Held, P., Dockhorn, A., and Kruse, R. (2014)Generating Events for Dynamic Social Network Simulations. In Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 46–55.
@inproceedings{HelDoc2014a,
author = {Held, Pascal and Dockhorn, Alexander and Kruse, Rudolf},
booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems},
keywords = {Generating},
pages = {46--55},
title = {Generating Events for Dynamic Social Network Simulations},
year = 2014
}%0 Conference Paper
%1 HelDoc2014a
%A Held, Pascal
%A Dockhorn, Alexander
%A Kruse, Rudolf
%B Information Processing and Management of Uncertainty in Knowledge-Based Systems
%D 2014
%P 46--55
%R 10.1007/978-3-319-08855-6_6
%T Generating Events for Dynamic Social Network Simulations
%U https://link.springer.com/chapter/10.1007/978-3-319-08855-6_6
%@ 978-3-319-08855-6