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Publications

Displaying results 131 to 140 of 668.

  1. Xiaoting Shao; Karl Stelzner; Kristian Kersting

    Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2202.00391, Pages 0-10, arXiv, 2022.

  2. Arseny Skryagin; Wolfgang Stammer; Daniel Ochs; Devendra Singh Dhami; Kristian Kersting

    Neural-Probabilistic Answer Set Programming

    In: Gabriele Kern-Isberner; Gerhard Lakemeyer; Thomas Meyer (Hrsg.). Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning. International Conference on Principles of Knowledge Representation and Reasoning (KR-2022), July 31 - August 5, Haifa, Israel, IJCAI Organization, 2022.

  3. Nandini Ramanan; Mayukh Das; Kristian Kersting; Sriraam Natarajan

    Discriminative Non-Parametric Learning of Arithmetic Circuits

    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 353-364, Proceedings of Machine Learning Research, Vol. 138, PMLR, 2020.

  4. Lukas Struppek; Dominik Hintersdorf; Daniel Neider; Kristian Kersting

    Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash

    In: FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT-22), June 21-24, Seoul, Korea, Republic of, Pages 58-69, ISBN 978-1-4503-9352-2, ACM, 2022.

  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. Caroline v. Dresky; Claus von der Burchard; Julia Andresen; Marc Steffen Seibel; Marc Rowedder; Timo Kepp; Johann Roider; Heinz Handels

    Visual acuity assessment from optical coherence tomography images using the foundation model RETFound

    In: Medical Imaging 2025: Computer-Aided Diagnosis. SPIE Medical Imaging (SPIE-2025), San Diego, United States, Vol. 13407, SPIE, 2025.

  10. Sudhanshu Mittal; Joshua Niemeijer; Özgün Çiçek; Maxim Tatarchenko; Jan Ehrhardt; Jörg P. Schäfer; Heinz Handels; Thomas Brox

    Realistic Evaluation of Deep Active Learning for Image Classification and Semantic Segmentation

    In: International Journal of Computer Vision (IJCV), Vol. 133, Pages 4294-4316, Springer Nature, 2025.

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