Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods-Reference-Cited by-同舟云学术

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Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods

Published:2024-05-06 Issue:2 Volume:6 Page:400-427
ISSN:2577-0187
Container-title:SIAM Journal on Mathematics of Data Science
language:en
Short-container-title:SIAM Journal on Mathematics of Data Science

Author:

Gerbelot Cédric¹,Troiani Emanuele²,Mignacco Francesca³,Krzakala Florent⁴,Zdeborová Lenka²

Affiliation:

1. Laboratoire de Physique de l’Ecole Normale Supérieure, Université PSL, Paris, France, and Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 USA.

2. Statistical Physics of Computation Lab, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, France.

3. Université Paris-Saclay, CNRS, CEA, Institut de physique théorique, 91191, Gif-sur-Yvette, France.

4. Information, Learning and Physics Lab, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, France.

Funder

Swiss National Science Foundation

Horizon 2020 Framework Programme

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference41 articles.

1. Out-of-equilibrium dynamical mean-field equations for the perceptron model

2. Aging of spherical spin glasses

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