Hopfield Layer

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

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.

Source: Hopfield Networks is All You Need

Latest Papers

PAPER DATE
Hopfield Networks is All You Need
| Hubert RamsauerBernhard SchäflJohannes LehnerPhilipp SeidlMichael WidrichLukas GruberMarkus HolzleitnerMilena PavlovićGeir Kjetil SandveVictor GreiffDavid KreilMichael KoppGünter KlambauerJohannes BrandstetterSepp Hochreiter
2020-07-16

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