# DeepProbLog: Neural Probabilistic Logic Programming

@inproceedings{Manhaeve2018DeepProbLogNP, title={DeepProbLog: Neural Probabilistic Logic Programming}, author={Robin Manhaeve and Sebastijan Dumancic and Angelika Kimmig and Thomas Demeester and Luc De Raedt}, booktitle={BNAIC/BENELEARN}, year={2018} }

We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports both symbolic and subsymbolic representations and inference, 1) program induction, 2) probabilistic (logic) programming, and 3) (deep) learning from examples. To the best of our knowledge, this work is the first to…

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## References

SHOWING 1-10 OF 77 REFERENCES

### ProbLog2: Probabilistic Logic Programming

- Computer ScienceECML/PKDD
- 2015

ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog, is presented, which offers both command line access to inference and learning and a Python library for building statistical relational learning applications from the system's components.

### Neural Logic Machines

- Computer ScienceICLR
- 2019

The Neural Logic Machine is proposed, a neural-symbolic architecture for both inductive learning and logic reasoning that achieves perfect generalization in a number of tasks, from relational reasoning tasks on the family tree and general graphs, to decision making tasks including sorting arrays, finding shortest paths, and playing the blocks world.

### NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language

- Computer ScienceACL
- 2019

A model combining neural networks with logic programming in a novel manner for solving multi-hop reasoning tasks over natural language by using an Prolog prover to utilize a similarity function over pretrained sentence encoders and fine-tune the representations for the similarity function via backpropagation.

### TensorLog : Deep Learning Meets Probabilistic Databases

- Computer Science
- 2017

An implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neuralnetwork infrastructure such as Tensorflow or Theano, which enables high-performance deep learning frameworks to be used for tuning the parameters of a Probabilistic logic.

### Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction

- Computer ScienceNeurIPS
- 2018

The model is used to synthesize functions on lists, edit text, and solve symbolic regression problems, showing how the model learns a domain-specific library of program components for expressing solutions to problems in the domain.

### Learning Relational Representations with Auto-encoding Logic Programs

- Computer ScienceIJCAI
- 2019

A novel framework for relational representation learning that combines the best of both worlds, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the original and latent representation is done by means of logic programs instead of neural networks.

### Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures

- Computer ScienceJ. Artif. Intell. Res.
- 2018

A lifted framework in which first-order rules are used to describe the structure of a given problem setting, which allows for a declarative specification of latent relational structures, which can then be efficiently discovered in a given data set using neural network learning.

### Inference and learning in probabilistic logic programs using weighted Boolean formulas

- Computer ScienceTheory and Practice of Logic Programming
- 2014

The results show that the inference algorithms improve upon the state of the art in probabilistic logic programming, and that it is indeed possible to learn the parameters of a probabilist logic program from interpretations.

### End-to-end Differentiable Proving

- Computer ScienceNIPS
- 2017

It is demonstrated that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.

### Neural Programmer-Interpreters

- Computer ScienceICLR
- 2016

The neural programmer-interpreter (NPI) is proposed, a recurrent and compositional neural network that learns to represent and execute programs and has the capability to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models.