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Publications

Displaying results 221 to 230 of 676.

  1. Carlos E. Luis; Alessandro G. Bottero; Julia Vinogradska; Felix Berkenkamp; Jan Peters

    Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2409.16824, Pages 1-31, arXiv, 2024.

  2. Carlo D'Eramo; Davide Tateo; Andrea Bonarini; Marcello Restelli; Jan Peters

    Sharing Knowledge in Multi-Task Deep Reinforcement Learning

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2401.09561, Pages 1-18, arXiv, 2024.

  3. Guillaume Duret; Mohamed Mahmoud Sayed Shelkamy Ali; Nicolas Cazin; Danylo Mazurak; Anna Samsonenko; Alexandre Chapin; Florence Zara; Emmanuel Dellandréa; Liming Chen; Jan Peters

    FruitBin: A Tunable Large-Scale Dataset for Advancing 6D Pose Estimation in Fruit Bin-Picking Automation

    In: Alessio Del Bue; Cristian Canton; Jordi Pont-Tuset; Tatiana Tommasi (Hrsg.). Computer Vision - ECCV 2024 Workshops - Milan, Italy, September 29-October 4, 2024, Proceedings, Part I. Computer Vision Systems (CVS), Pages 73-90, Lecture Notes in Computer Science, Vol. 15623, Springer, 2024.

  4. Julen Urain; Ajay Mandlekar; Yilun Du; Mahi Shafiullah; Danfei Xu; Katerina Fragkiadaki; Georgia Chalvatzaki; Jan Peters

    Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2408.04380, Pages 1-20, arXiv, 2024.

  5. Daniel Hernández-Lobato; Viktoriia Sharmanska; Kristian Kersting; Christoph H. Lampert; Novi Quadrianto

    Mind the Nuisance: Gaussian Process Classification using Privileged Noise

    In: Zoubin Ghahramani; Max Welling; Corinna Cortes; Neil D. Lawrence; Kilian Q. Weinberger (Hrsg.). Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014. Neural Information Processing Systems (NeurIPS-2014), December 8-13, Montreal, Quebec, Canada, Pages 837-845, Curran Associates, Inc. 2014.

  6. Daniel Geißler; Bo Zhou; Paul Lukowicz

    Latent Inspector: An Interactive Tool for Probing Neural Network Behaviors Through Arbitrary Latent Activation

    In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence (IJCAI-2023), August 19-25, Macao, Macao, ISBN 978-1-956792-03-4, International Joint Conferences on Artificial Intelligence, 2023.

  7. 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.

  8. 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 Uncertainty in Artificial Intelligence (UAI-2019), July 22-25, Tel Aviv, Israel, Pages 334-344, Proceedings of Machine Learning Research, Vol. 115, AUAI Press, 2019.

  9. 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 Principles and Practice of Knowledge Discovery in Databases (ECML PKDD-2019), International Workshops of ECML PKDD 2019, Proceedings, Part I, September 16-20, Würzburg, Germany, Pages 28-43, Communications in Computer and Information Science, Vol. 1167, Springer, 2019.

  10. 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 Inductive Logic Programming (ILP-2019), September 3-5, Plovdiv, Bulgaria, Pages 62-71, Lecture Notes in Computer Science (LNAI), Vol. 11770, Springer, 2019.

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