Eigenvector Dreaming

Marco Benedetti; Louis Carillo; Enzo Marinari; Marc Mèzard

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arXiv · 2308.13445

Eigenvector Dreaming

Marco Benedetti·Louis Carillo·Enzo Marinari·Marc Mèzard

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Abstract

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.

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cond-mat.dis-nn cs.NE

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Marco Benedetti, Louis Carillo, Enzo Marinari, Marc Mèzard. 2023-08-09. Eigenvector Dreaming. https://arxiv.org/abs/2308.13445

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