Minimum Description Length Hopfield Networks

Autor:	Abudy, Matan, Lan, Nur, Chemla, Emmanuel, Katzir, Roni
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Associative memory architectures are designed for memorization but also offer, through their retrieval method, a form of generalization to unseen inputs: stored memories can be seen as prototypes from this point of view. Focusing on Modern Hopfield Networks (MHN), we show that a large memorization capacity undermines the generalization opportunity. We offer a solution to better optimize this tradeoff. It relies on Minimum Description Length (MDL) to determine during training which memories to store, as well as how many of them. Comment: 4 pages, Associative Memory & Hopfield Networks Workshop at NeurIPS2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.06518 Zobrazit plný text záznamu View this record from Arxiv