A differentiable approach to inductive logic programming

Recent work in neural abstract machines has proposed many useful techniques to learn sequences of applications of discrete but differentiable operators. These techniques allow us to model traditionally procedural problems using neural networks. In this work, we are interested in using neural networks to learn to perform logic reasoning. We propose a model that has access to differentiable operators which can be composed to perform reasoning. These differentiable reasoning operators were first introduced in TensorLog, a recently proposed probabilistic deductive database. Equipped with a model than can perform logic reasoning, we further investigate the task of inductive logic programming.