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Year: 2020
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Displaying results 41 to 50 of 69.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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 0-10, Frontiers, 2020.

  10. 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.

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