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DeepProbLog: Neural Probabilistic Logic Programming
Katholieke Universiteit Leuven, Leuven, Belgium.
Katholieke Universiteit Leuven, Leuven, Belgium.
Cardiff University, Cardiff, England.
Ghent University, Ghent, Belgium.
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2018 (English)In: Advances in Neural Information Processing Systems 31 (NIPS 2018) / [ed] S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett, Neural Information Processing Systems Foundation Inc. , 2018, p. 3753-3760Conference paper, Published paper (Refereed)
Abstract [en]

We introduce DeepProbLog, a probabilistic logic programming language that in-corporates 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 sub-symbolic 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.

Place, publisher, year, edition, pages
Neural Information Processing Systems Foundation Inc. , 2018. p. 3753-3760
Series
Advances in Neural Information Processing Systems, ISSN 1049-5258 ; 31
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-90778ISI: 000461823303072OAI: oai:DiVA.org:oru-90778DiVA, id: diva2:1540438
Conference
32nd Conference on Neural Information Processing Systems (NIPS 2018), Montreal, Canada, December 2-8, 2018
Note

Funding Agencies:

FWO 1S61718N G079416N

KU Leuven GOA/13/010

Available from: 2021-03-29 Created: 2021-03-29 Last updated: 2021-03-29Bibliographically approved

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De Raedt, Luc

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