DeepStochLog: Neural Stochastic Logic Programming

• URL: http://arxiv.org/abs/2106.12574v1
• Date: Wed, 23 Jun 2021 17:59:04 GMT
• Title: DeepStochLog: Neural Stochastic Logic Programming
• Authors: Thomas Winters, Giuseppe Marra, Robin Manhaeve, Luc De Raedt
• Abstract summary: We show that inference and learning in neural logic programming scale much better than for neural probabilistic logic programs. DeepStochLog achieves state-of-the-art results on challenging neural symbolic learning tasks.
• Score: 15.938755941588159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in neural symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose DeepStochLog, an alternative neural symbolic framework based on stochastic definite clause grammars, a type of stochastic logic program, which defines a probability distribution over possible derivations. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to create a framework that can be trained end-to-end. We show that inference and learning in neural stochastic logic programming scale much better than for neural probabilistic logic programs. Furthermore, the experimental evaluation shows that DeepStochLog achieves state-of-the-art results on challenging neural symbolic learning tasks.

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