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arXiv · 2007.02876

A Mathematical Theory of Attention

James Vuckovic·Aristide Baratin·Remi Tachet des Combes
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Abstract

Attention is a powerful component of modern neural networks across a wide variety of domains. However, despite its ubiquity in machine learning, there is a gap in our understanding of attention from a theoretical point of view. We propose a framework to fill this gap by building a mathematically equivalent model of attention using measure theory. With this model, we are able to interpret self-attention as a system of self-interacting particles, we shed light on self-attention from a maximum entropy perspective, and we show that attention is actually Lipschitz-continuous (with an appropriate metric) under suitable assumptions. We then apply these insights to the problem of mis-specified input data; infinitely-deep, weight-sharing self-attention networks; and more general Lipschitz estimates for a specific type of attention studied in concurrent work.

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James Vuckovic, Aristide Baratin, Remi Tachet des Combes. 2020-07-20. A Mathematical Theory of Attention. https://arxiv.org/abs/2007.02876

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