# Unsupervised Learning via Total Correlation Explanation

arXiv: https://arxiv.org/abs/1706.08984 · HTML: https://mlsanity.com/paper/1706.08984/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2017.

## Built on by (most cited first, 3 of 3)

- [Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series](https://mlsanity.com/paper/2101.03850/) (2021)
- [Maximum Total Correlation Reinforcement Learning](https://mlsanity.com/paper/2505.16734/) (2025)
- [Video and Language Alignment in 2D Systems for 3D Multi-object Scenes with Multi-Information Derivative-Free Control](https://mlsanity.com/paper/2512.24826/) (2025)

## Abstract (arXiv)

Learning by children and animals occurs effortlessly and largely without obvious supervision. Successes in automating supervised learning have not translated to the more ambiguous realm of unsupervised learning where goals and labels are not provided. Barlow (1961) suggested that the signal that brains leverage for unsupervised learning is dependence, or redundancy, in the sensory environment. Dependence can be characterized using the information-theoretic multivariate mutual information measure called total correlation. The principle of Total Cor-relation Ex-planation (CorEx) is to learn representations of data that "explain" as much dependence in the data as possible. We review some manifestations of this principle along with successes in unsupervised learning problems across diverse domains including human behavior, biology, and language.