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Publications

Displaying results 271 to 280 of 682.

  1. Vitor Fortes Rey; Pedro Martelleto Bressane Rezende; Bo Zhou; Sungho Suh; Paul Lukowicz

    COA-HAR: Exploring contrastive online test-time adaptation for wearable sensor-based human activity recognition using sensor data augmentation

    In: Expert Systems with Applications (ESWA), Vol. 297, Page 129288, Elsevier, 2026.

  2. Mohammad Minouei; Reza Soheili; Didier Stricker

    Multimodal approach for imbalanced document classification

    In: Wolfgang Osten (Hrsg.). Seventeenth International Conference on Machine Vision (ICMV 2024). International Conference on Machine Vision (ICMV-2024), October 10-13, Edinburg, United Kingdom, Pages 347-358, SPIE Conference Proceedings, Vol. 13517, SPIE, 2024.

  3. Julen Urain; Ajay Mandlekar; Yilun Du; Nur Muhammad (Mahi) Shafiullah; Danfei Xu; Katerina Fragkiadaki; Georgia Chalvatzaki; Jan Peters

    A Survey on Deep Generative Models for Robot Learning From Multimodal Demonstrations

    In: IEEE Transactions on Robotics and Automation, Vol. 42, Pages 60-79, ArXiv, 2026.

  4. Shreedhar Govil; Didier Stricker; Jason Raphael Rambach

    DriverGaze360: OmniDirectional Driver Attention with Object-Level Guidance

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). International Conference on Computer Vision and Pattern Recognition (CVPR), June 3-7, Denver, Colorado, United States Minor Outlying Islands, IEEE, 2026.

  5. Amos Treiber; Alejandro Molina; Christian Weinert; Thomas Schneider; Kristian Kersting

    CryptoSPN: Expanding PPML beyond Neural Networks

    In: Benyu Zhang; Raluca Ada Popa; Matei Zaharia; Guofei Gu; Shouling Ji (Hrsg.). PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice. Workshop on Privacy-Preserving Machine Learning in Practice (PPMLP-20), located at CS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security, November 9, Virtual Event, Pages 9-14, ISBN 978-1-4503-8088-1, ACM, 2020.

  6. Patrick Schramowski; Wolfgang Stammer; Stefano Teso; Anna Brugger; Franziska Herbert; Xiaoting Shao; Hans-Georg Luigs; Anne-Katrin Mahlein; Kristian Kersting

    Making deep neural networks right for the right scientific reasons by interacting with their explanations

    In: Nature Machine Intelligence, Vol. 2, No. 8, Pages 476-486, Springer, 2020.

  7. Johannes Czech; Moritz Willig; Alena Beyer; Kristian Kersting; Johannes Fürnkranz

    Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data

    In: Frontiers in Artificial Intelligence, Vol. 3, Pages 1-10, Frontiers, 2020.

  8. Hikaru Shindo; Devendra Singh Dhami; Kristian Kersting

    Neuro-Symbolic Forward Reasoning

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2110.09383, Pages 0-10, arXiv, 2021.

  9. Zhongjie Yu; Fabrizio Ventola; Kristian Kersting

    Whittle Networks: A Deep Likelihood Model for Time Series

    In: Marina Meila; Tong Zhang (Hrsg.). Proceedings of the 38th International Conference on Machine Learning. International Conference on Machine Learning (ICML-2021), July 18-24, Pages 12177-12186, Proceedings of Machine Learning Research, Vol. 139, PMLR, 2021.

  10. Wolfgang Stammer; Patrick Schramowski; Kristian Kersting

    Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their Explanations

    In: IEEE Conference on Computer Vision and Pattern Recognition. International Conference on Computer Vision and Pattern Recognition (CVPR-2021), June 19-25, Pages 3619-3629, Computer Vision Foundation / IEEE, 2021.

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