Sparse and Structured Hopfield Networks

• URL: http://arxiv.org/abs/2402.13725v2
• Date: Tue, 4 Jun 2024 22:04:40 GMT
• Title: Sparse and Structured Hopfield Networks
• Authors: Saul Santos, Vlad Niculae, Daniel McNamee, Andre F. T. Martins,
• Abstract summary: We provide a unified framework for sparse Hopfield networks by establishing a link with Fenchel-Young losses. We reveal a connection between loss margins, sparsity, and exact memory retrieval. Experiments on multiple instance learning and text rationalization demonstrate the usefulness of our approach.
• Score: 14.381907888022612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern Hopfield networks have enjoyed recent interest due to their connection to attention in transformers. Our paper provides a unified framework for sparse Hopfield networks by establishing a link with Fenchel-Young losses. The result is a new family of Hopfield-Fenchel-Young energies whose update rules are end-to-end differentiable sparse transformations. We reveal a connection between loss margins, sparsity, and exact memory retrieval. We further extend this framework to structured Hopfield networks via the SparseMAP transformation, which can retrieve pattern associations instead of a single pattern. Experiments on multiple instance learning and text rationalization demonstrate the usefulness of our approach.

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