[2023] [2022] [2021] [2020] [2019] [2018] [2017] [2016] [2015] [2014]

2023

  • Adhisantoso, Y. G., Voges, J., and Ostermann, J. (2023)PEKORA: High-Performance 3D Genome Reconstruction Using K-th Order Spearman’s Rank Correlation Approximation [Talk]. In ISMB/ECCB 2023.
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  • 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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  • Zhu, J., Awiszus, M., Cook, M., Dockhorn, A., Eberhardinger, M., Loiacono, D., Lucas, S. M., Matran-Fernandez, A., Liebana, D. P., Thompson, T., and Veltkamp, R. (2023)Explainable AI for Games, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 73–75.
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  • Olson, C., Wagner, L., and Dockhorn, A. (2023)Evolutionary Optimization of Baba Is You Agents. In 2023 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8.
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  • Kuhnke, F., and Ostermann, J. (2023)Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency, IEEE Transactions on Biometrics, Behavior, and Identity Science.
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  • Xu, L., Dockhorn, A., and Perez-Liebana, D. (2023)Elastic Monte Carlo Tree Search, IEEE Transactions on Games.
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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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.
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  • 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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  • Dockhorn, A., Eberhardinger, M., Loiacono, D., Liebana, D. P., and Veltkamp, R. (2023)Pokegen, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 39–42.
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  • 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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  • 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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  • 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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  • 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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  • 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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  • L{ü}dtke, U., Bornman, J., de Wet, F., Heid, U., Ostermann, J., Rumberg, L., der Linde, J. V., and Ehlert, H. (2023)Multidisciplinary Perspectives on Automatic Analysis of Children’s Language Samples: Where Do We Go from Here?, Folia Phoniatrica et Logopaedica 75, 1–12.
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  • 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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  • 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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  • 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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  • 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.
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  • 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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  • Adhisantoso, Y. G. (2023)Cross-check of Philips’s Response to Core Experiment for Annotation Data Indexing using B-Tree m62224, ISO/IEC JTC 1/SC 29/WG 8.
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  • 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.
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  • 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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  • Krause, L. M. K., Manderfeld, E., Gnutt, P., Vogler, L., Wassick, A., Richard, K., Rudolph, M., Hunsucker, K. Z., Swain, G. W., Rosenhahn, B., and Rosenhahn, A. (2023)Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure, Biofouling 39, 64–79.
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  • Adhisantoso, Y. G., Voges, J., Rohlfing, C., Tunev, V., Ohm, J.-R., and Ostermann, J. (2023)GVC: Efficient Random Access Compression for Gene Sequence Variations. In BMC Bioinformatics, p. 13.
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  • 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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  • Chang, Y., Ren, Z., Nguyen, T. T., Qian, K., and Schuller, B. W. (2023)Knowledge transfer for on-device speech emotion recognition with neural structured learning. In ICASSP.
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  • Ren, Z., Nguyen, T. T., Chang, Y., and Schuller, B. W. (2023)Fast yet effective speech emotion recognition with self-distillation. In ICASSP.
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  • 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.
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2022

  • Grimm, E., Kuhnke, F., Gajdt, A., Ostermann, J., and Knoche, M. (2022)Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis, Horticulturae 8.
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  • 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.
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  • 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.
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  • 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.
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  • Moosbauer, J., Casalicchio, G., Lindauer, M., and Bischl, B. (2022)Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution, Arxiv Preprint.
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  • 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).
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  • 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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  • Ren, Z., Chang, Y., Bartl-Pokorny, K. D., Pokorny, F. B., and Schuller, B. W. (2022)The acoustic dissection of cough: Diving into machine listening-based COVID-19 analysis and detection, Journal of Voice 1–14.
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  • 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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  • 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).
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  • 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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  • 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.
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  • Stahl, M., Spliethöver, M., and Wachsmuth, H. (2022)To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science.
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  • 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.
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  • Alshomary, M., El Baff, R., Gurcke, T., and Wachsmuth, H. (2022)The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 8782–8797.
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  • 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.
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  • 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.
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  • 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, arXiv.
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  • Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B., and Lindauer, M. (2022)POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning, Arxiv Preprint, arXiv.
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  • Mohan, A., Ruhkopf, T., and Lindauer, M. (2022)Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML), arXiv.
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  • Sass, R., Bergman, E., Biedenkapp, A., Hutter, F., and Lindauer, M. (2022)DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML), arXiv.
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  • 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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  • Benjamins, C., Jankovic, A., Raponi, E., van der Blom, K., Lindauer, M., and Doerr, C. (2022)Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. In Workshop on Meta-Learning (MetaLearn 2022).
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  • 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.
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  • 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.
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  • 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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  • 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.
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  • 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}.
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  • 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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  • Dockhorn, A., and Kruse, R. (2022)Balancing Exploration and Exploitation in Forward Model Learning, Advances in Intelligent Systems Research and Innovation 1–19.
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  • Adhisantoso, Y. G. (2022)Recommendation on MPEG-G Part 6 Record Structure m61340, ISO/IEC JTC 1/SC 29/WG 8.
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  • 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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  • Nguyen, D., Henschel, R., Rosenhahn, B., Sonntag, D., and Swoboda, P. (2022)LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking. In Computer Vision and Pattern Recognition (CVPR), pp. 1–10.
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  • Kellermann, C., Selmi, A., Brown, D., and Ostermann, J. (2022)Fault Detection in Multi-stage Manufacturing to Improve Process Quality. In International Conference on Control, Automation and Diagnosis (ICCAD), pp. 1–6.
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  • Basso, L., Ren, Z., and Nejdl, W. (2022)Towards Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks, CoRR abs/2211.02678.
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  • 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.
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  • 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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  • Moosbauer, J., Casalicchio, G., Lindauer, M., and Bischl, B. (2022)Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution, arXiv.
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  • Chang, Y., Ren, Z., Nguyen, T. T., Nejdl, W., and Schuller, B. W. (2022)Example-based explanations with adversarial attacks for respiratory sound analysis. In Interspeech 2022, 23rd Annual Conference of the International Speech Communication Association, Incheon, Korea, 18-22 September 2022, pp. 4003–4007, {ISCA}.
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  • 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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  • 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.
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  • Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., and Lindauer, M. (2022)MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information.
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  • 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.
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  • 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 .
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  • Tang, M., Antic, Z., Pietzsch, S., Lentes, J., Hofmann, W., Cario, G., Escherich, G., Udo Zu Stadt, U., Schlegelberger, B., Horstmann, M., Stanulla, M., and Bergmann, A. K. (2022)Analyzing clinical RNAseq data with machine learning models greatly improves the genetic diagnosis in pediatric acute lymphoblastic leukemia. In .
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  • 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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  • 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.
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  • Deng, D., and Lindauer, M. (2022)Searching in the Forest for Local Bayesian Optimization. In ECML/PKDD workshop on Meta-learning.
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  • Dockhorn, A., and Kruse, R. (2022)State and Action Abstraction for Search and Reinforcement Learning Algorithms, pp. 1–18, Springer International Publishing.
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  • 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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  • 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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  • 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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  • 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.
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  • 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.
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  • 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.
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  • Iosifidis, V., Papadopulous, S., Rosenhahn, B., and Ntoutsi, E. (2022)AdaCC: cumulative cost-sensitive boosting for imbalanced classification, Knowledge and Information Systems 38.
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  • Dockhorn, A. (2022)Choosing Representation, Mutation, and Crossover in Genetic Algorithms, IEEE Computational Intelligence Magazine 17, 52–53.
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  • Rosenboom, I., Scheithauer, T., Friedrich, F. C., P{ö}rtner, S., Hollstein, L., Pust, M., Sifakis, K., Wehrbein, T., Rosenhahn, B., Wiehlmann, L., Chhatwal, P., T{ü}mmler, B., and Davenport, C. F. (2022)Wochenende - modular and flexible alignment-based shotgun metagenome analysis, BMC Genomics.
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  • Kaiser, T., Ehmann, L., Reinders, C., and Rosenhahn, B. (2022)Blind Knowledge Distillation for Robust Image Classification, arXiv.
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  • Voges, J. (2022)Compression of DNA Sequencing Data, Fortschritt-Berichte VDI.
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  • Hinrichs, R., Jiang, N., Beltran, R., Krause, T., K{ä}ding, M., Lange, A., Schmidt, B., Ostermann, J., and Marx, S. (2022)Analysis of the Repeatability of the Pencil Lead Break in Comparison to the Ball Impact and Electromagnetic Body-Noise Actuator. In 20th World Conference on Non-Destructive Testing (WCNDT 2020).
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  • Reinders, C., and Rosenhahn, B. (2022)Adversarial Attacks and Defenses in Deep Learning, We Are Developers!.
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  • Reinders, C., Schubert, F., and Rosenhahn, B. (2022)ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing. In Arxiv Preprint.
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  • 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.
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  • 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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  • 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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  • 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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  • Schubert, F., Benjamins, C., D{ö}hler, S., Rosenhahn, B., and Lindauer, M. (2022)POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning, Arxiv Preprint.
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  • 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.
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  • 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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  • 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}.
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  • Adhisantoso, Y. G., and Ostermann, J. (2022)Contact Matrix Compressor. In 2022 Data Compression Conference (DCC), pp. 399–408.
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  • 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.
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  • 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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  • 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.
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  • 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.
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  • Rumberg, L., Gebauer, C., Ehlert, H., L{ü}dtke, U., and Ostermann, J. (2022)Improving Phonetic Transcriptions of Children’s Speech by Pronunciation Modelling with Constrained CTC-Decoding. In Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association.
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  • 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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  • 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.
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  • 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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  • Alshomary, M., and Stahl, M. (2022)Argument Novelty and Validity Assessment via Multitask and Transfer Learning. In Proceedings of the 9th Workshop on Argument Mining, pp. 111–114, International Conference on Computational Linguistics.
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  • Awiszus, M., Schubert, F., and Rosenhahn, B. (2022)Wor(l)d-GAN: Towards Natural Language Based PCG in Minecraft, IEEE Transactions on Games.
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  • 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.
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  • 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.
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  • 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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  • 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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  • 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.
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  • Schier, M., Reinders, C., and Rosenhahn, B. (2022)Constrained Mean Shift Clustering. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM).
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  • 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.
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  • Hinrichs, R., Liang, K., Lu, Z., and Ostermann, J. (2022)Improved Compression of Artificial Neural Networks through Curvature-Aware Training. In Proceedings of the IEEE World Congress on Computational Intelligence.
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  • Mast, M., Marschollek, M., Jack, T., Wulff, A., and Elise Study, G. (2022)Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data, Stud Health Technol Inform 289, 228–231.
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  • 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.
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  • 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.
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  • Pestel-Schiller, U., and Ostermann, J. (2022)Impact of Spatial Resolution and Zoom on Interpreter-Based Evaluation of Compressed SAR Images. In 14th European Conference on Synthetic Aperture Radar.
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  • 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.
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  • Chen, W.-F., Chen, M.-H., Mudgal, G., and Wachsmuth, H. (2022)Analyzing Culture-Specific Argument Structures in Learner Essays. In Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), pp. 51–61.
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  • Bode, L., Schamer, S., Bohnke, J., Bott, O. J., Marschollek, M., Jack, T., Wulff, A., and Group, E. S. (2022)Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction, Appl Clin Inform 13, 1002–1014.
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  • 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.
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  • 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.
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  • 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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  • Hinrichs, R., Heise, H., and Ehmann, L. O. (2022)Lossless Compression at Zero Delay of the Electrical Stimulation Patterns of Cochlear Implants for Wireless Streaming of Audio Using Artificial Neural Networks. In 7th International Conference on Frontiers of Signal Processing.
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  • 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..
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  • Xu, R., K{ä}ding, M., Lange, A., Ostermann, J., and Marx, S. (2022)Detection of impulsive signals on tendons for hybrid wind turbines using acoustic emission measurements. In International Symposium on Non-Destructive Testing in Civil Engineering (NDT-CE 2022).
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  • Lange, A., Hinrichs, R., and Ostermann, J. (2022)Localized Damage Detection in Wind Turbine Rotor Blades using Airborne Acoustic Emissions (accepted). In 9th Asia-Pacific Workshops on Structural Health Monitoring 2022 (APWSHM 2022).
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  • 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).
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  • Gebauer, C., Dengler, N., and Bennewitz, M. (2022)Sensor-Based Navigation Using Hierarchical Reinforcement Learning. In International Conference on Intelligent Autonomous Systems (IAS).
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2021

  • 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).
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  • 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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  • Adhisantoso, Y. G. (2021)Verification of the Extension to the Coding of Contact Matrix m58073, ISO/IEC JTC 1/SC 29/WG 8.
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  • Adhisantoso, Y. G. (2021)Cross-check CE3 Extension of Contact Matrix Compressor m58074, ISO/IEC JTC 1/SC 29/WG 8.
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  • 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.
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  • 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.
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  • 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.
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  • 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).
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  • 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.
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  • 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.
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  • 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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  • 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).
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  • 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.
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  • 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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  • 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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  • Schubert, F., Eimer, T., Rosenhahn, B., and Lindauer, M. (2021)Automatic Risk Adaptation in Distributional Reinforcement Learning. In Arxiv Preprint.
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  • Awiszus, M., Schubert, F., and Rosenhahn, B. (2021)World-GAN: a Generative Model for Minecraft Worlds. In IEEE Conference on Games.
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  • 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.
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  • 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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  • 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).
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  • 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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  • 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).
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  • Adhisantoso, Y. G., and Ostermann, J. (2021)Efficient Coding of Contact Matrices m57789, ISO/IEC JTC 1/SC 29/WG 8.
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  • 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.
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  • 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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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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).
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  • 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.
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  • 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.
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  • 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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  • Apeldoorn, D., and Dockhorn, A. (2021)Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning, IEEE Transactions on Games 13, 249–262.
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  • 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.
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  • 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.
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  • Dockhorn, A., and Kruse, R. (2021)Modelheuristics for efficient forward model learning, At-Automatisierungstechnik.
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  • 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.
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  • 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.
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  • Dockhorn, A., and Kruse, R. (2021)Fuzzy Modeling in Game AI, Journal of Pure and Applied Mathematics 12, 54–68.
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  • 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).
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  • 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).
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  • 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.
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  • 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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  • 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.
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  • 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.
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  • 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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  • 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.
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  • 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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  • 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.
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  • 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.
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  • 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).
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  • 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.
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  • 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.
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  • 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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  • Adhisantoso, Y. G., and Ostermann, J. (2021)Method for the Coding of Contact Matrix m56622, ISO/IEC JTC 1/SC 29/WG 8.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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).
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  • 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.
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  • 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.
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  • 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.
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  • Hutter, F., Fuks, L., Lindauer, M., and Awad, N. (2021)Method, device and computer program for producing a strategy for a robot.
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  • 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.
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  • 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).
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  • 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.
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  • 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).
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  • 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.
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  • 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.
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  • 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.
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  • 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}.
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  • 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.
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  • 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.
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  • 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)}.
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  • 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}.
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  • 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.
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  • 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.
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  • 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.
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  • 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 .
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  • 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.
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  • 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.
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  • 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) ).
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  • 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.
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  • 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.
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2020

  • 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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  • 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.
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  • 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).
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  • 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.
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  • 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.
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  • 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.
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  • Rudolph, M., Wandt, B., and Rosenhahn, B. (2020, August)Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows..
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  • Gebauer, C., and Bennewitz, M. (2020)Penalized Bootstrapping for Reinforcement Learning in Robot Control. In International Conference on Machine Learning and Applications (CMLA).
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  • 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.
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  • Dockhorn, A. (2020)Dissertation: Prediction-based Search for Autonomous Game-Playing, Otto von Guericke University Magdeburg 1–231.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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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  • 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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  • 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.
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  • Dockhorn, A. (2020)Vorhersagebasierte Suche f{ü}r autonomes Spielen, pp. 69–78, GI.
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  • 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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  • 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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  • {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.
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  • 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.
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  • 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.
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  • Krause, L., Koc, J., Rosenhahn, B., and Rosenhahn, A. (2020)Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure, Environmental Science and Technology 54, 10022–10030.
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  • 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.
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  • 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.
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  • Awiszus, M., Schubert, F., and Rosenhahn, B. (2020, October)TOAD-GAN: Coherent Style Level Generation from a Single Example.
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  • 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.
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  • 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.
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  • 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.
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  • 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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  • J{ü}rgens, H., Hinrichs, R., and Ostermann, J. (2020)Recognizing Guitar Effects and Their Parameter Settings. In Proceedings of the DAFx2020 (Vol I).
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  • 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.
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  • Zell, P., Rosenhahn, B., and Wandt, B. (2020)Weakly-supervised Learning of Human Dynamics. In European Conference on Computer Vision (ECCV).
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  • 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).
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  • 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).
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  • 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).
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  • 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.
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  • Meuel, H., and Ostermann, J. (2020)Analysis of Affine Motion-Compensated Prediction in Video Coding, IEEE Transactions on Image Processing 29, 7359–7374.
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  • 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.
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  • 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.
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  • 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.
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  • 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).
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  • Kluger, F., Ackermann, H., Yang, M. Y., and Rosenhahn, B. (2020)Temporally Consistent Horizon Lines. In International Conference on Robotics and Automation (ICRA).
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  • Krause, T., and Ostermann, J. (2020)Damage Detection for Wind Turbine Rotor Blades Using Airborne Sound, Structural Control and Health Monitoring.
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  • 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.
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  • Ostermann, J., and Hinrichs, R. (2020)Links und rechts verbinden, Unimagazin.
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  • 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.
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  • 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).
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  • Ackermann, H., Meuel, H., Rosenhahn, B., and Ostermann, J. (2020)Verfahren und Vorrichtung zum Aufnehmen eines Digitalbildes 1–12.
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  • 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.
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  • 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).
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  • Eggensperger, K., Haase, K., M{ü}ller, P., Lindauer, M., and Hutter, F. (2020)Neural Model-based Optimization with Right-Censored Observations. In CoRR.
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  • 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.
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  • 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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  • 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].
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  • 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).
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  • Souza, A., Nardi, L., Oliveira, L., Olukotun, K., Lindauer, M., and Hutter, F. (2020)Prior-guided Bayesian Optimization. In arxiv:2006.14608[cs.LG].
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  • 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).
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  • 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.
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  • 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).
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  • 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.
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2019

  • 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.
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  • Dockhorn, A., and Mostaghim, S. (2019)Introducing the Hearthstone-AI Competition, arXiv:1906.04238 1–4.
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  • 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.
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  • 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.
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  • 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).
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  • Ostermann, J., Denkena, B., Bergmann, B., Schmidt, A., Krause, T., and Voges, J. (2019)Compression of Machine Tool Data, ISO/IEC JTC1/SC29/WG11.
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  • 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.
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2018

  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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2017

  • 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.
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  • 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.
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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.
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  • Dockhorn, A., Braune, C., and Kruse, R. (2016)Variable density based clustering. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8.
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2015

  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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  • Dockhorn, A. (2015)Master Thesis: Hierarchical Extensions and Cluster Validation Techniques for DBSCAN, Otto von Guericke University Magdeburg 1–80.
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  • 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.
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2014

  • Dockhorn, A. (2014)Bachelor Thesis: Computergest{ü}tzte Analyse onkologischer Daten mithilfe Graphischer Modelle, Otto von Guericke University of Magdeburg 1–80.
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  • 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.
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  • 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).
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