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Publications

Displaying results 291 to 300 of 682.

  1. Karl Stelzner; Robert Peharz; Kristian Kersting

    Faster Attend-Infer-Repeat with Tractable Probabilistic Models

    In: Kamalika Chaudhuri; Ruslan Salakhutdinov (Hrsg.). Proceedings of the 36th International Conference on Machine Learning. International Conference on Machine Learning (ICML-2019), June 9-15, Long Beach, California, USA, Pages 5966-5975, Proceedings of Machine Learning Research, Vol. 97, PMLR, 2019.

  2. Andrea Galassi; Kristian Kersting; Marco Lippi; Xiaoting Shao; Paolo Torroni

    Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

    In: Frontiers in Big Data, Vol. 2 - 2019, Pages 0-10, Frontiers, 1/2020.

  3. Kristian Kersting; Miryung Kim; Guy Van den Broeck; Thomas Zimmermann

    SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091)

    In: Dagstuhl Reports, Vol. 10, No. 2, Pages 76-87, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2020.

  4. 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.

  5. 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.

  6. 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.

  7. Alejandro Molina; Patrick Schramowski; Kristian Kersting

    Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks

    In: 8th International Conference on Learning Representations. International Conference on Learning Representations (ICLR-2020), April 26-30, Addis Ababa, Ethiopia, OpenReview.net, 2020.

  8. 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.

  9. David Steinmann; Matej Zecevic; Devendra Singh Dhami; Kristian Kersting

    Machines Explaining Linear Programs

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2206.07194, Pages 0-10, arXiv, 2022.

  10. Xiaoting Shao; Kristian Kersting

    Gradient-based Counterfactual Explanations using Tractable Probabilistic Models

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2205.07774, Pages 0-10, arXiv, 2022.

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