Analysis of gradient descent on wide two-layer ReLU neural networks (L. Chizat, EPFL)
Analysis of gradient descent on wide two-layer ReLU neural networks
Speaker:Dr. Lénaïc Chizat
Affiliation: CNRS, Université Paris-Sud, Website
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Meeting ID: 615 4539 3381
Passcode: 304949
Abstract: In this talk, we propose an analysis of gradient descent on wide two-layer ReLU neural networks that leads to sharp characterizations of the learned predictor. The main idea is to study the training dynamics when the width of the hidden layer goes to infinity, which is a Wasserstein gradient flow. While this dynamics evolves on a non-convex landscape, we show that for appropriate initializations, its limit, when it exists, is a global minimizer. We also study the implicit regularization of this algorithm when the objective is the unregularized logistic loss, which leads to a max-margin classifier in a certain functional space. We finally discuss what these results tell us about the generalization performance, and in particular how these models compare to kernel methods.