113 citations · 638 across the 37 of their papers we have counts for
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The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
Thibault Séjourné, François-Xavier Vialard, Gabriel Peyré
Comparing metric measure spaces (i.e. a metric space endowed with aprobability distribution) is at the heart of many machine learning problems. The most popular distance between su…
A Shooting Formulation of Deep Learning
François-Xavier Vialard, Roland Kwitt, Susan Wei +1
Continuous-depth neural networks can be viewed as deep limits of discrete neural networks whose dynamics resemble a discretization of an ordinary differential equation (ODE). Altho…
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence
Lenaic Chizat, Pierre Roussillon, Flavien Léger +2
The squared Wasserstein distance is a natural quantity to compare probability distributions in a non-parametric setting. This quantity is usually estimated with the plug-in estimat…