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- W4287114105 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." @default.
- W4287114105 created "2022-07-25" @default.
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- W4287114105 date "2021-06-23" @default.
- W4287114105 modified "2023-09-24" @default.
- W4287114105 title "DeepStochLog: Neural Stochastic Logic Programming" @default.
- W4287114105 doi "https://doi.org/10.48550/arxiv.2106.12574" @default.
- W4287114105 hasPublicationYear "2021" @default.
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