TASI Lectures on Physics for Machine Learning

Jim Halverson

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arXiv · 2408.00082

TASI Lectures on Physics for Machine Learning

Jim Halverson

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Abstract

These notes are based on lectures I gave at TASI 2024 on Physics for Machine Learning. The focus is on neural network theory, organized according to network expressivity, statistics, and dynamics. I present classic results such as the universal approximation theorem and neural network / Gaussian process correspondence, and also more recent results such as the neural tangent kernel, feature learning with the maximal update parameterization, and Kolmogorov-Arnold networks. The exposition on neural network theory emphasizes a field theoretic perspective familiar to theoretical physicists. I elaborate on connections between the two, including a neural network approach to field theory.

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hep-th cs.LG hep-ph

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BibTeX RIS

Jim Halverson. 2024-07-31. TASI Lectures on Physics for Machine Learning. https://arxiv.org/abs/2408.00082

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