Minimum Description Length Hopfield Networks
Autor: | Abudy, Matan, Lan, Nur, Chemla, Emmanuel, Katzir, Roni |
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Rok vydání: | 2023 |
Předmět: | |
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 |
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