# Neural Markov Prolog

arXiv: https://arxiv.org/abs/2312.01521 · HTML: https://mlsanity.com/paper/2312.01521/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [From Statistical Relational to Neuro-Symbolic Artificial Intelligence](https://mlsanity.com/paper/2003.08316/) (2020)
- [Neural Probabilistic Logic Programming in Discrete-Continuous Domains](https://mlsanity.com/paper/2303.04660/) (2023)

## Abstract (arXiv)

The recent rapid advance of AI has been driven largely by innovations in neural network architectures. A concomitant concern is how to understand these resulting systems. In this paper, we propose a tool to assist in both the design of further innovative architectures and the simple yet precise communication of their structure. We propose the language Neural Markov Prolog (NMP), based on both Markov logic and Prolog, as a means to both bridge first order logic and neural network design and to allow for the easy generation and presentation of architectures for images, text, relational databases, or other target data types or their mixtures.