# Eigenvector Dreaming

arXiv: https://arxiv.org/abs/2308.13445 · HTML: https://mlsanity.com/paper/2308.13445/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2023.

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

- [Minimal Effective Theory for Phonotactic Memory: Capturing Local Correlations due to Errors in Speech](https://mlsanity.com/paper/2309.02466/) (2023)

## Abstract (arXiv)

Among the performance-enhancing procedures for Hopfield-type networks that implement associative memory, Hebbian Unlearning (or dreaming) strikes for its simplicity and its clear biological interpretation. Yet, it does not easily lend itself to a clear analytical understanding. Here we show how Hebbian Unlearning can be effectively described in terms of a simple evolution of the spectrum and the eigenvectors of the coupling matrix. We use these ideas to design new dreaming algorithms that are effective from a computational point of view, and are analytically far more transparent than the original scheme.