Skip to main content Skip to main navigation

Publications

 

Due to maintenance work, it is currently not possible to search for publications by author.

Search narrowed by:

Year: 2019
Remove all filters
Displaying results 1 to 10 of 62.

  1. 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.

  2. 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.

  3. 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 …

  4. 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 …

  5. 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 …

  6. 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 …

  7. 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 …

  8. 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, …

  9. Michael Lutter; Boris Belousov; Kim Listmann; Debora Clever; Jan Peters

    HJB optimal feedback control with deep differential value functions and action constraints

    In: Leslie Pack Kaelbling; Danica Kragic; Komei Sugiura (Hrsg.). 3rd Annual Conference on Robot Learning. Conference on Robot Learning (CoRL-2019), …

  10. Nathaniel Weir; Andrew Crotty; Alex Galakatos; Amir Ilkhechi; Shekar Ramaswamy; Rohin Bhushan; Ugur Çetintemel; Prasetya Utama; Nadja Geisler; Benjamin Hättasch; Steffen Eger; Carsten Binnig

    DBPal: Weak Supervision for Learning a Natural Language Interface to Databases

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1909.06182, Pages 0-10, arXiv, 2019.

Year