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@article{KuhOst2023,
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
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%A Kuhnke, Felix
%A Ostermann, J{ö}rn
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%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 - URLBibTeXEndNoteBibSonomyPoker, 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 - URLBibTeXEndNoteBibSonomyMast, 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.
@article{RN11,
author = {Mast, M. and Marschollek, M. and Jack, T. and Wulff, A. and Elise Study, Group},
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keywords = {l3s},
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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
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%A Marschollek, M.
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%A Wulff, A.
%A Elise Study, Group
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%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
%V 289 - URLBibTeXEndNoteBibSonomyGrimm, 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.
@article{GriKuh2022,
author = {Grimm, Eckhard and Kuhnke, Felix and Gajdt, Anna and Ostermann, J{ö}rn and Knoche, Moritz},
journal = {Horticulturae},
keywords = {Flesh},
month = {01},
number = 2,
title = {Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis},
volume = 8,
year = 2022
}%0 Journal Article
%1 GriKuh2022
%A Grimm, Eckhard
%A Kuhnke, Felix
%A Gajdt, Anna
%A Ostermann, J{ö}rn
%A Knoche, Moritz
%D 2022
%J Horticulturae
%N 2
%R https://doi.org/10.3390/horticulturae8020145
%T Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis
%U https://www.mdpi.com/2311-7524/8/2/145
%V 8 - BibTeXEndNoteBibSonomyParker-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.
@article{2201.03916,
author = {Parker-Holder, Jack and Rajan, Raghu and Song, Xingyou and Biedenkapp, André and Miao, Yingjie and Eimer, Theresa and Zhang, Baohe and Nguyen, Vu and Calandra, Roberto and Faust, Aleksandra and Hutter, Frank and Lindauer, Marius},
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 - BibTeXEndNoteBibSonomyRumberg, 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.
@inproceedings{RumGeb2022b,
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 - BibTeXEndNoteBibSonomyPestel-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.
@inproceedings{PesOst2022,
author = {Pestel-Schiller, Ulrike and Ostermann, J{ö}rn},
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 - URLBibTeXEndNoteBibSonomyMoosbauer, 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 - AbstractBibTeXEndNoteBibSonomyBondarenko, 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
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%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
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%I CEUR WS
%J CEUR Workshop Proceedings
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%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. - URLBibTeXEndNoteBibSonomyXu, 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).
@inproceedings{XuHur2022,
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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
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%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
%U https://arxiv.org/abs/2205.15126 - URLBibTeXEndNoteBibSonomyDockhorn, 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.
@article{DocKir2022,
author = {Dockhorn, Alexander and Kirst, Martin and Mostaghim, Sanaz and Wieczorek, Martin and Zille, Heiner},
journal = {IEEE Transactions on Games},
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
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@article{DocKru2022,
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
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%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
%@ 978-3-030-78124-8 - BibTeXEndNoteBibSonomyReinders, C., Schubert, F., and Rosenhahn, B. (2022)ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing. In Arxiv Preprint.
@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
}%0 Conference Paper
%1 ReiSch2022
%A Reinders, Christoph
%A Schubert, Frederik
%A Rosenhahn, Bodo
%B Arxiv Preprint
%D 2022
%T ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing - URLBibTeXEndNoteBibSonomyBenjamins, 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.
@article{https://doi.org/10.48550/arxiv.2202.04500,
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
%U https://arxiv.org/abs/2202.04500 - URLBibTeXEndNoteBibSonomySchier, M., Reinders, C., and Rosenhahn, B. (2022)Constrained Mean Shift Clustering. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM).
@inproceedings{SchRei2022a,
author = {Schier, Maximilian and Reinders, Christoph and Rosenhahn, Bodo},
booktitle = {Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)},
keywords = {Clustering},
month = {04},
title = {Constrained Mean Shift Clustering},
year = 2022
}%0 Conference Paper
%1 SchRei2022a
%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
%U https://github.com/m-schier/cms - BibTeXEndNoteBibSonomyHinrichs, 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.
@inproceedings{HinLia2022,
author = {Hinrichs, Reemt and Liang, Kai and Lu, Ze and Ostermann, J{ö}rn},
booktitle = {Proceedings of the IEEE World Congress on Computational Intelligence},
keywords = {Curvature-Aware},
month = {07},
title = {Improved Compression of Artificial Neural Networks through Curvature-Aware Training},
year = 2022
}%0 Conference Paper
%1 HinLia2022
%A Hinrichs, Reemt
%A Liang, Kai
%A Lu, Ze
%A Ostermann, J{ö}rn
%B Proceedings of the IEEE World Congress on Computational Intelligence
%D 2022
%T Improved Compression of Artificial Neural Networks through Curvature-Aware Training - BibTeXEndNoteBibSonomyStahl, 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.
@inproceedings{Stahl_Spliethöver_Wachsmuth,
author = {Stahl, Maja and Spliethöver, Maximilian and Wachsmuth, Henning},
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
}%0 Conference Paper
%1 Stahl_Spliethöver_Wachsmuth
%A Stahl, Maja
%A Spliethöver, Maximilian
%A Wachsmuth, Henning
%B Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science
%D 2022
%T To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation - BibTeXEndNoteBibSonomyAlshomary, 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.
@inproceedings{Alshomary_Stahl_2022,
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 - URLBibTeXEndNoteBibSonomySass, 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.
@inproceedings{https://doi.org/10.48550/arxiv.2206.03493,
author = {Sass, René and Bergman, Eddie and Biedenkapp, André and Hutter, Frank and Lindauer, Marius},
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
%U https://arxiv.org/abs/2206.03493 - URLBibTeXEndNoteBibSonomyBenjamins, 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).
@inproceedings{benjamins2022towards,
author = {Benjamins, Carolin and Jankovic, Anja and Raponi, Elena and van der Blom, Koen and Lindauer, Marius and Doerr, Carola},
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
%U https://www.ai.uni-hannover.de/de/forschung/publikationen/publikationen-detailansicht?tx_t3luhpublications_publications%5Baction%5D=show&tx_t3luhpublications_publications%5Bcontroller%5D=Publication&tx_t3luhpublications_publications%5Bpublication%5D=7783&cHash=04b8ffd50e56727ae4181c8e2a2261f1 - BibTeXEndNoteBibSonomyChen, 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.
@inproceedings{Chen_Chen_Mudgal_Wachsmuth_2022,
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
}%0 Conference Paper
%1 Chen_Chen_Mudgal_Wachsmuth_2022
%A Chen, Wei-Fan
%A Chen, Mei-Hua
%A Mudgal, Garima
%A Wachsmuth, Henning
%B Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)
%D 2022
%P 51–61
%T Analyzing Culture-Specific Argument Structures in Learner Essays - BibTeXEndNoteBibSonomyBiedenkapp, 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 - BibTeXEndNoteBibSonomyAlshomary, 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.
@inproceedings{alshomary2022moral,
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
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%A Gurcke, Timon
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%T The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments - AbstractURLBibTeXEndNoteBibSonomyHolzapfel, 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. - AbstractURLBibTeXEndNoteBibSonomyWu, 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. - URLBibTeXEndNoteBibSonomyDockhorn, 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},
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%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 - AbstractBibTeXEndNoteBibSonomyKaushal, 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
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%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. - URLBibTeXEndNoteBibSonomyDockhorn, A., and Kruse, R. (2021)Modelheuristics for efficient forward model learning, At-Automatisierungstechnik.
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author = {Dockhorn, Alexander and Kruse, Rudolf},
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%U https://www.degruyter.com/document/doi/10.1515/auto-2021-0037/html - BibTeXEndNoteBibSonomyKluger, 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
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%T Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images - BibTeXEndNoteBibSonomyPestel-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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author = {Pestel-Schiller, Ulrike and Ostermann, J{ö}rn},
booktitle = {13th European Conference on Synthetic Aperture Radar},
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title = {Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability},
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%T Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability - URLBibTeXEndNoteBibSonomyHutter, 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},
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%A Fuks, Lior
%A Lindauer, Marius
%A Awad, Noor
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%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 - URLBibTeXEndNoteBibSonomySchubert, 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
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%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 - BibTeXEndNoteBibSonomyBenjak, 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)},
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note = {accepted for publication},
title = {Enhanced Machine Learning-based Inter Coding for VVC},
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%D 2021
%T Enhanced Machine Learning-based Inter Coding for VVC - BibTeXEndNoteBibSonomyTennakoon, 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},
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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},
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%D 2021
%P 2865-2874
%R 10.1109/CVPR46437.2021.00289
%T Consensus Maximisation Using Influences of Monotone Boolean Functions - BibTeXEndNoteBibSonomyIlyas, 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 - BibTeXEndNoteBibSonomyHu, 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
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%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 - BibTeXEndNoteBibSonomyTan, 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
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%A Maybery, Murray
%A Gilani, Syed Zulqarnain
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%A Mian, Ajmal
%A Suter, David
%A Whitehouse, Andrew
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%J Translational Psychiatry
%N 1
%R 10.1038/s41398-020-0695-z
%T A broad autism phenotype expressed in facial morphology
%V 10 - URLBibTeXEndNoteBibSonomyDockhorn, 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)},
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%@ 9781728154565 - BibTeXEndNoteBibSonomyMinh, C. N. D., Gilani, S. Z., Islam, S., and Suter, D. (2020)Learning Affordance Segmentation: An Investigative Study. In DICTA2020.
@inproceedings{chauDICTA2020,
author = {Minh, Chau Nguyen Duc and Gilani, Syed Zulqarnain and Islam, Syed and Suter, David},
booktitle = {DICTA2020},
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title = {Learning Affordance Segmentation: An Investigative Study},
year = 2020
}%0 Conference Paper
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%A Minh, Chau Nguyen Duc
%A Gilani, Syed Zulqarnain
%A Islam, Syed
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%R 10.1109/DICTA51227.2020.9363390
%T Learning Affordance Segmentation: An Investigative Study - URLBibTeXEndNoteBibSonomyGra{{\"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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author = {Gra{{\"s}}hof, Stella and Ackermann, Hanno and Brandt, Sami and Ostermann, J{ö}rn},
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month = {04},
note = {early access},
title = {Multilinear Modelling of Faces and Expressions},
year = 2020
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%T Multilinear Modelling of Faces and Expressions
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@book{Doc2020,
author = {Dockhorn, Alexander},
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%@ 978-3-88579-775-3 - URLBibTeXEndNoteBibSonomyOstermann, J., and Hinrichs, R. (2020)Links und rechts verbinden, Unimagazin.
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%U https://anyflip.com/cjox/dool/ - URLBibTeXEndNoteBibSonomyDockhorn, 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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%U http://ceur-ws.org/Vol-2862/ - BibTeXEndNoteBibSonomyJ{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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%T Recognizing Guitar Effects and Their Parameter Settings - URLBibTeXEndNoteBibSonomyAwiszus, 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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%U /brokenurl#AAAI, github, arxiv, Award - BibTeXEndNoteBibSonomySamayoa, 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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%T Parameter Selection for a Video Communication System based on HEVC and Channel Coding - URLBibTeXEndNoteBibSonomyHu, 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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%V 12323 - URLBibTeXEndNoteBibSonomyDockhorn, 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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%@ 978-94-6252-770-6 - BibTeXEndNoteBibSonomyDengel, 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,
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%T QUEST ON BEXUS 27 - URLBibTeXEndNoteBibSonomyHeld, 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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