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Jahr: 2019
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  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Nils M. Kriege; Marion Neumann; Christopher Morris; Kristian Kersting; Petra Mutzel

    A unifying view of explicit and implicit feature maps of graph kernels

    In: Data Mining and Knowledge Discovery, Vol. 33, No. 6, Pages 1505-1547, Springer, 2019.

  6. Laura Antanas; Plinio Moreno; Marion Neumann; Rui Pimentel de Figueiredo; Kristian Kersting; José Santos-Victor; Luc De Raedt

    Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach

    In: Autonomous Robots, Vol. 43, No. 6, Pages 1393-1418, Springer, 2019.

  7. Mayukh Das; Devendra Singh Dhami; Gautam Kunapuli; Kristian Kersting; Sriraam Natarajan

    Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs

    In: The Thirty-Third AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence (AAAI-2019), Pages 7816-7824, AAAI Press, 2019.

  8. Antonio Vergari; Alejandro Molina; Robert Peharz; Zoubin Ghahramani; Kristian Kersting; Isabel Valera

    Automatic Bayesian Density Analysis

    In: The Thirty-Third AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence (AAAI-2019), Pages 5207-5215, AAAI Press, 2019.

  9. Anna Brugger; Jan Behmann; Stefan Paulus; Hans-Georg Luigs; Matheus Thomas Kuska; Patrick Schramowski; Kristian Kersting; Ulrike Steiner; Anne-Katrin Mahlein

    Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range

    In: Remote Sensing, Vol. 11, No. 12, Pages 0-10, MDPI, 2019.

  10. Fabrizio Riguzzi; Kristian Kersting; Marco Lippi; Sriraam Natarajan

    Editorial: Statistical Relational Artificial Intelligence

    In: Frontiers in Robotics and AI, Vol. 6, Pages 0-10, Frontiers, 2019.

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