# Coulomb Autoencoders

arXiv: https://arxiv.org/abs/1802.03505 · HTML: https://mlsanity.com/paper/1802.03505/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2018.

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

- [Inter-domain Multi-relational Link Prediction](https://mlsanity.com/paper/2106.06171/) (2021)

## Abstract (arXiv)

Learning the true density in high-dimensional feature spaces is a well-known problem in machine learning. In this work, we consider generative autoencoders based on maximum-mean discrepancy (MMD) and provide theoretical insights. In particular, (i) we prove that MMD coupled with Coulomb kernels has optimal convergence properties, which are similar to convex functionals, thus improving the training of autoencoders, and (ii) we provide a probabilistic bound on the generalization performance, highlighting some fundamental conditions to achieve better generalization. We validate the theory on synthetic examples and on the popular dataset of celebrities' faces, showing that our model, called Coulomb autoencoders, outperform the state-of-the-art.