Les Houches lectures on deep learning at large and infinite width

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

Research output: Contribution to journalArticlepeer-review

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 the 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.

Original languageEnglish (US)
Article number104012
JournalJournal of Statistical Mechanics: Theory and Experiment
Volume2024
Issue number10
DOIs
StatePublished - Oct 31 2024

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • critical behavior of disordered systems
  • deep learning
  • machine learning
  • stochastic processes

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