ArticleProceedings of machine learning research2022
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models.
Article in Proceedings of machine learning research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Hippocampo-neocortical interaction as compressive retrieval-augmented generation.Nature communications · 2026Article
- A comparative study of nonlinear cleanup rules in resonator networks.Frontiers in artificial intelligence · 2026Article
- A sparse quantized hopfield network for online-continual memory.Nature communications · 2024Article
- A generative model of memory construction and consolidation.Nature human behaviour · 2024Article
- Sequential Memory with Temporal Predictive Coding.Advances in neural information processing systems · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
A large number of neural network models of associative memory have been proposed in the literature. These include the classical Hopfield networks (HNs), sparse distributed memories (SDMs), and more recently the modern continuous Hopfield networks (MCHNs), which possess close links with self-attention in machine learning. In this paper, we propose a general framework for understanding the operation of such memory networks as a sequence of three operations:
Identifiers
36751405PMC7614148What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.