# Neural Probabilistic Logic Programming in DeepProbLog

## TL;DR

DeepProbLog is a novel framework that integrates neural networks with probabilistic logic programming, enabling end-to-end training and combining symbolic reasoning with deep learning.

## Contribution

It introduces DeepProbLog, the first framework combining neural predicates with probabilistic logic programming for end-to-end learning and reasoning.

## Key findings

- Supports symbolic and subsymbolic inference
- Enables program induction and probabilistic reasoning
- Allows deep learning from examples within a logical framework

## Abstract

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.