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Publications

Displaying results 1901 to 1910 of 14838.

  1. Patrick Schramowski; Cigdem Turan; Sophie F. Jentzsch; Constantin A. Rothkopf; Kristian Kersting

    BERT has a Moral Compass: Improvements of ethical and moral values of machines

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1912.05238, Pages 0-10, arXiv, 2019.

  2. Fabrizio Ventola; Karl Stelzner; Alejandro Molina; Kristian Kersting

    Random Sum-Product Forests with Residual Links

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1908.03250, Pages 0-10, arXiv, 2019.

  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 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.

  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 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.

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

  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 on Machine Learning (ICML-2019), June 9-15, Long Beach, California, USA, Pages 5966-5975, Proceedings of Machine Learning Research, Vol. 97, PMLR, 2019.

  8. Sriraam Natarajan; Jose Picado; Tushar Khot; Kristian Kersting; Christopher Ré; Jude W. Shavlik

    Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge

    In: Jesse Davis; Jan Ramon (Hrsg.). Inductive Logic Programming - 24th International Conference. International Conference on Inductive Logic Programming (ILP-2014), September 14-16, Nancy, France, Pages 92-107, Lecture Notes in Computer Science, Vol. 9046, Springer, 2014.

  9. Marion Neumann; Lisa Hallau; Benjamin Klatt; Kristian Kersting; Christian Bauckhage

    Erosion Band Features for Cell Phone Image Based Plant Disease Classification

    In: 22nd International Conference on Pattern Recognition. International Conference on Pattern Recognition (ICPR-2014), August 24-28, Stockholm, Sweden, Pages 3315-3320, IEEE Computer Society, 2014.

  10. Shuo Yang; Tushar Khot; Kristian Kersting; Gautam Kunapuli; Kris Hauser; Sriraam Natarajan

    Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach

    In: Ravi Kumar; Hannu Toivonen; Jian Pei; Joshua Zhexue Huang; Xindong Wu (Hrsg.). 2014 IEEE International Conference on Data Mining. IEEE International Conference on Data Mining (ICDM-2014), December 14-17, Shenzhen, China, Pages 1085-1090, IEEE Computer Society, 2014.

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