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Publications

Displaying results 161 to 170 of 682.

  1. Tanya Amit Tyagi

    Critical Evaluation of Biologically Informed Neural Networks: Validating Biological Pathway Representation

    Mastersthesis, University of Saarland, 2026.

  2. Hans-Georg Fill; Jennifer Horkoff; Peter Fettke; Julius Köppke

    Generative AI and Conceptual Modeling

    In: Oliver Hinz (Hrsg.). Business & Information Systems Engineering (BISE), Vol. 68, Pages 1-5, Springer Nature LINK, Wiesbaden, 2/2026.

  3. 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.

  4. Sara Farmahini Farahani

    Towards Personalized Cancer Immunotherapy: A Deep Learning Approach for Tumor-Specific T Cell Receptor Discovery

    Mastersthesis, University of Saarland, 2/2026.

  5. Saifullah Saifullah; Stefan Agne; Andreas Dengel; Sheraz Ahmed

    The Reality of High Performing Deep Learning Models: A Case Study on Document Image Classification

    In: IEEE Access (IEEE), Vol. 12, Pages 103537-103564, IEEE, 2024.

  6. 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.

  7. 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.

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

    Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2001.05371, Pages 0-10, arXiv, 2020.

  9. Xiaoting Shao; Alejandro Molina; Antonio Vergari; Karl Stelzner; Robert Peharz; Thomas Liebig; Kristian Kersting

    Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures

    In: Manfred Jaeger; Thomas Dyhre Nielsen (Hrsg.). Proceedings of the 10th International Conference on Probabilistic Graphical Models. International Conference on Probabilistic Graphical Models (PGM-2020), September 23-25, Aalborg, Denmark, Pages 401-412, Proceedings of Machine Learning Research, Vol. 138, PMLR, 2020.

  10. Robert Peharz; Steven Lang; Antonio Vergari; Karl Stelzner; Alejandro Molina; Martin Trapp; Guy Van den Broeck; Kristian Kersting; Zoubin Ghahramani

    Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

    In: Proceedings of the 37th International Conference on Machine Learning. International Conference on Machine Learning (ICML-2020), July 13-18, Pages 7563-7574, Proceedings of Machine Learning Research, Vol. 119, PMLR, 2020.

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