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• W3039502308 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." @default.
• W3039502308 created "2020-07-10" @default.
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• W3039502308 date "2020-07-06" @default.
• W3039502308 modified "2023-09-27" @default.
• W3039502308 title "A Mathematical Theory of Attention" @default.
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• W3039502308 hasPublicationYear "2020" @default.
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