2 citations · 5 across the 16 of their papers we have counts for
3 papers · 2 filters
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré
Conservation laws are well-established in the context of Euclidean gradient flow dynamics, notably for linear or ReLU neural network training. Yet, their existence and principles f…
Understanding the training of infinitely deep and wide ResNets with Conditional Optimal Transport
Raphaël Barboni, Gabriel Peyré, François-Xavier Vialard
We study the convergence of gradient flow for the training of deep neural networks. If Residual Neural Networks are a popular example of very deep architectures, their training con…
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
Zhenzhang Ye, Gabriel Peyré, Daniel Cremers +1
Bilevel optimization aims to optimize an outer objective function that depends on the solution to an inner optimization problem. It is routinely used in Machine Learning, notably f…