DeepStochLog: Neural Stochastic Logic Programming

Understand

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.