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We introduce DeepProbLog, a probabilistic logic programming language thatincorporates deep learning by means of neural predicates. We show how existinginference and learning techniques can be adapted for the new language. Ourexperiments demonstrate that DeepProbLog supports both symbolic and subsymbolicrepresentations and inference, 1) program induction, 2) probabilistic (logic)programming, and 3) (deep) learning from examples. To the best of ourknowledge, this work is the first to propose a framework where general-purposeneural networks and expressive probabilistic-logical modeling and reasoning areintegrated in a way that exploits the full expressiveness and strengths of bothworlds and can be trained end-to-end based on examples.

DeepProbLog: Neural Probabilistic Logic Programming