Publikationen

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.

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.

Lukas Weber; Lukas Sommer; Julian Oppermann; Alejandro Molina; Kristian Kersting; Andreas Koch

Resource-Efficient Logarithmic Number Scale Arithmetic for SPN Inference on FPGAs

In: International Conference on Field-Programmable Technology. International Conference on Field Programmable Technology (FPT-2019), December 9-13, Tianjin, China, Pages 251-254, IEEE, 2019.

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

Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices

In: Vincent Conitzer; Gillian K. Hadfield; Shannon Vallor (Hrsg.). Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. AAAI Conference on Artificial Intelligence (AAAI-2019), January 27-28, Honolulu, HI, USA, Pages 37-44, ACM, 2019.

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.

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.

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.

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.

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.

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.