# Optimal Binary Autoencoding with Pairwise Correlations

arXiv: https://arxiv.org/abs/1611.02268 · HTML: https://mlsanity.com/paper/1611.02268/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2016.

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

- [Entropy, concentration, and learning: a statistical mechanics primer](https://mlsanity.com/paper/2409.18630/) (2024)

## Abstract (arXiv)

We formulate learning of a binary autoencoder as a biconvex optimization problem which learns from the pairwise correlations between encoded and decoded bits. Among all possible algorithms that use this information, ours finds the autoencoder that reconstructs its inputs with worst-case optimal loss. The optimal decoder is a single layer of artificial neurons, emerging entirely from the minimax loss minimization, and with weights learned by convex optimization. All this is reflected in competitive experimental results, demonstrating that binary autoencoding can be done efficiently by conveying information in pairwise correlations in an optimal fashion.