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Publications

Displaying results 811 to 820 of 15217.

  1. Piotr Szymanski; Tomasz Kajdanowicz; Kristian Kersting

    How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?

    In: Entropy, Vol. 18, No. 8, Pages 0-10, MDPI, 2016.

  2. Alejandro Molina; Antonio Vergari; Nicola Di Mauro; Sriraam Natarajan; Floriana Esposito; Kristian Kersting

    Sum-Product Networks for Hybrid Domains

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1710.03297, Pages 0-10, arXiv, 2017.

  3. Alejandro Molina; Alexander Munteanu; Kristian Kersting

    Coresets for Dependency Networks

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1710.03285, Pages 0-10, arXiv, 2017.

  4. Christopher Morris; Kristian Kersting; Petra Mutzel

    Global Weisfeiler-Lehman Graph Kernels

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1703.02379, Pages 0-10, arXiv, 2017.

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

    A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1703.00676, Pages 0-10, arXiv, 2017.

  6. Zhao Xu; Romain Vial; Kristian Kersting

    Graph Enhanced Memory Networks for Sentiment Analysis

    In: Michelangelo Ceci; Jaakko Hollmén; Ljupco Todorovski; Celine Vens; Saso Dzeroski (Hrsg.). Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD-2017), September 18-22, Skopje, Macedonia, The Former Yugoslav Republic of, Pages 374-389, Lecture Notes in Computer Science, Vol. 10534, Springer, 2017.

  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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