Mean-field analysis of the training dynamics of two-layer neural networks
Date(s) : 09/12/2025 iCal
14h30 - 15h30
Training neural networks via stochastic gradient descent (SGD)
amounts to solving a complex, non-convex optimization problem.
Inspired by statistical mechanics, the mean-field approach provides
a macroscopic description of the training dynamics,
which can be formulated as a convex optimization problem.
In this talk, I will explain how to rigorously derive
the macroscopic (mean-field) description
from the microscopic dynamics given by the SGD updates.
More precisely, I will establish the mean-field limit
(a law of large numbers) and study the fluctuations
around this limit (a central limit theorem).
If time allows, I will present similar results
in the Bayesian framework.
Emplacement
I2M Saint-Charles - Salle de séminaire
Catégories
