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Year: 2019
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Displaying results 1 to 10 of 62.

  1. Michael Lutter; Kim Listmann; Jan Peters

    Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

    In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems …

  2. Michael Lutter; Christian Ritter; Jan Peters

    Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

    In: 7th International Conference on Learning Representations. International Conference on Learning Representations (ICLR-2019), May 6-9, New Orleans, …

  3. Alejandro Molina; Antonio Vergari; Karl Stelzner; Robert Peharz; Pranav Subramani; Nicola Di Mauro; Pascal Poupart; Kristian Kersting

    SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1901.03704, Pages 0-10, arXiv, 2019.

  4. Robert Peharz; Antonio Vergari; Karl Stelzner; Alejandro Molina; Martin Trapp; Xiaoting Shao; Kristian Kersting; Zoubin Ghahramani

    Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

    In: Amir Globerson; Ricardo Silva (Hrsg.). Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence. Conference in …

  5. Claas Völcker; Alejandro Molina; Johannes Neumann; Dirk Westermann; Kristian Kersting

    DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets

    In: Peggy Cellier; Kurt Driessens (Hrsg.). Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and …

  6. Navdeep Kaur; Gautam Kunapuli; Saket Joshi; Kristian Kersting; Sriraam Natarajan

    Neural Networks for Relational Data

    In: Dimitar Kazakov; Can Erten (Hrsg.). Inductive Logic Programming - 29th International Conference, Proceedings. International Conference on …

  7. 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 …

  8. Michael Benedikt; Kristian Kersting; Phokion G. Kolaitis; Daniel Neider

    Logic and Learning (Dagstuhl Seminar 19361)

    In: Dagstuhl Reports, Vol. 9, No. 9, Pages 1-22, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2019.

  9. Patrick Lübbecke; Nijat Mehdiyev; Peter Fettke

    Substitution of hazardous chemical substances using Deep Learning and t-SNE

    In: Proceedings der Internationalen Tagung Wirtschaftsinformatik. Internationale Tagung Wirtschaftsinformatik (WI-2019), Human Practice. Digital …

  10. Patrick Helber; Benjamin Bischke; Andreas Dengel; Damian Borth

    Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

    In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2019.

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