Neural Probabilistic Logic Programming in DeepProbLog
  We introduce DeepProbLog, a neural probabilistic logic programming language
that incorporates deep learning by means of neural predicates. We show how
existing inference and learning techniques of the underlying probabilistic
logic programming language ProbLog can be adapted for the new language. We
theoretically and experimentally demonstrate that DeepProbLog supports (i) both
symbolic and subsymbolic 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.
