Methods › General › Attention Modules › Hopfield Layer

Hopfield Layer

3 papers tagged archive 2025-07-28

Introduced by Hubert Ramsauer et al. in Hopfield Networks is All You Need

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

A Hopfield Layer is a module that enables a network to associate two sets of vectors. This general functionality allows for transformer-like self-attention, for decoder-encoder attention, for time series prediction (maybe with positional encoding), for sequence analysis, for multiple instance learning, for learning with point sets, for combining data sources by associations, for constructing a memory, for averaging and pooling operations, and for many more.

In particular, the Hopfield layer can readily be used as plug-in replacement for existing layers like pooling layers (max-pooling or average pooling, permutation equivariant layers, GRU & LSTM layers, and attention layers. The Hopfield layer is based on modern Hopfield networks with continuous states that have very high storage capacity and converge after one update.

PaperSourceSee Code · ml-jku/hopfield-layers

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Action Unit Detection1
All1
Drug Design1
Facial Action Unit Detection1
Immune Repertoire Classification1
Multiple Instance Learning1
Relation1
Representation Learning1
Retrieval1

Usage over time archive 2025-07-28

Papers per year tagged with Hopfield Layer: 2020 to 2021, peak 2 2 0 2020: 2 papers 2020 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Attention ModulesRecurrent Neural NetworksPooling Operations

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