43 citations · 190 across the 11 of their papers we have counts for
6 papers · 1 filter
Learning strides in convolutional neural networks
Rachid Riad, Olivier Teboul, David Grangier +1
Convolutional neural networks typically contain several downsampling operators, such as strided convolutions or pooling layers, that progressively reduce the resolution of intermed…
Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein
Marco Cuturi, Laetitia Meng-Papaxanthos, Yingtao Tian +3
Optimal transport tools (OTT-JAX) is a Python toolbox that can solve optimal transport problems between point clouds and histograms. The toolbox builds on various JAX features, suc…
Learning with Differentiable Perturbed Optimizers
Quentin Berthet, Mathieu Blondel, Olivier Teboul +3
Machine learning pipelines often rely on optimization procedures to make discrete decisions (e.g., sorting, picking closest neighbors, or shortest paths). Although these discrete d…
Supervised Quantile Normalization for Low-rank Matrix Approximation
Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed +1
Low rank matrix factorization is a fundamental building block in machine learning, used for instance to summarize gene expression profile data or word-document counts. To be robust…
MULEX: Disentangling Exploitation from Exploration in Deep RL
Lucas Beyer, Damien Vincent, Olivier Teboul +3
An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the…
Differentiable Ranks and Sorting using Optimal Transport
Marco Cuturi, Olivier Teboul, Jean-Philippe Vert
Sorting an array is a fundamental routine in machine learning, one that is used to compute rank-based statistics, cumulative distribution functions (CDFs), quantiles, or to select…