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DeepProbLog: neural probabilistic logic programming

Manhaeve, Robin, Dumancic, Sebastijan, Kimmig, Angelika, Demeester, Thomas and De Raedt, Luc 2018. DeepProbLog: neural probabilistic logic programming. Presented at: 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, QC, Canada, 3-8 December 2018. Published in: Bengio, Samy, Wallach, Hanna M., Larochelle, Hugo, Grauman, Kristen and Cesa-Bianchi, Nicolo eds. NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., pp. 3753-3763.

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Abstract

We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Curran Associates Inc.
Related URLs:
Date of First Compliant Deposit: 6 November 2018
Last Modified: 08 Oct 2021 14:21
URI: http://orca.cardiff.ac.uk/id/eprint/116510

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