# Renormalized Mutual Information for Artificial Scientific Discovery

arXiv: https://arxiv.org/abs/2005.01912 · HTML: https://mlsanity.com/paper/2005.01912/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 1 of 1)

- [REMEDI: Corrective Transformations for Improved Neural Entropy Estimation](https://mlsanity.com/paper/2402.05718/) (2024)

## Abstract (arXiv)

We derive a well-defined renormalized version of mutual information that allows to estimate the dependence between continuous random variables in the important case when one is deterministically dependent on the other. This is the situation relevant for feature extraction, where the goal is to produce a low-dimensional effective description of a high-dimensional system. Our approach enables the discovery of collective variables in physical systems, thus adding to the toolbox of artificial scientific discovery, while also aiding the analysis of information flow in artificial neural networks.