32 citations · 90 across the 19 of their papers we have counts for
35 papers
Gauss-Newton drifting extends natural gradient descent
Théo Dumont, Théo Lacombe, François-Xavier Vialard
Drifting methods have recently been introduced as a promising new paradigm for training generative models, and have attracted a lot of attention so far. In this work, we focus on h…
Decreasing Entropic Regularization Averaged Gradient for Semi-Discrete Optimal Transport
Ferdinand Genans, Antoine Godichon-Baggioni, François-Xavier Vialard +1
Adding entropic regularization to Optimal Transport (OT) problems has become a standard approach for designing efficient and scalable solvers. However, regularization introduces a…
Stochastic Optimization in Semi-Discrete Optimal Transport: Convergence Analysis and Minimax Rate
Ferdinand Genans, Antoine Godichon-Baggioni, François-Xavier Vialard +1
We investigate the semi-discrete Optimal Transport (OT) problem, where a continuous source measure is transported to a discrete target measure , with particular attention to…
Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
Raphaël Barboni, Gabriel Peyré, François-Xavier Vialard
We study the convergence of gradient methods for the training of mean-field single-hidden-layer neural networks with square loss. For this high-dimensional and non-convex optimizat…
Nonnegative cross-curvature in infinite dimensions: synthetic definition and spaces of measures
Flavien Léger, Gabriele Todeschi, François-Xavier Vialard
Nonnegative cross-curvature (NNCC) is a geometric property of a cost function defined on a product space that originates in optimal transportation and the Ma-Trudinger-Wang theory.…
multiGradICON: A Foundation Model for Multimodal Medical Image Registration
Basar Demir, Lin Tian, Thomas Hastings Greer +7
Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast…