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arXiv · 2309.01592

Les Houches Lectures on Deep Learning at Large & Infinite Width

Yasaman Bahri·Boris Hanin·Antonin Brossollet·Vittorio Erba·Christian Keup·Rosalba Pacelli·James B. Simon
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Abstract

These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural networks. Topics covered include various statistical and dynamical properties of these networks. In particular, the lecturers discuss properties of random deep neural networks; connections between trained deep neural networks, linear models, kernels, and Gaussian processes that arise in the infinite-width limit; and perturbative and non-perturbative treatments of large but finite-width networks, at initialization and after training.

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Yasaman Bahri, Boris Hanin, Antonin Brossollet, Vittorio Erba, Christian Keup, Rosalba Pacelli, James B. Simon. 2023-09-04. Les Houches Lectures on Deep Learning at Large & Infinite Width. https://arxiv.org/abs/2309.01592

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