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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: Thirty-second Conference on Neural Information Processing Systems (NIPS 2018), Montreal, QC, Canada, 2-8 December 2018.

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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)
Status: Unpublished
Schools: Computer Science & Informatics
Related URLs:
Date of First Compliant Deposit: 6 November 2018
Last Modified: 04 Mar 2020 14:15
URI: http://orca-mwe.cf.ac.uk/id/eprint/116510

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