38 citations · 57 across the 7 of their papers we have counts for
6 papers · 1 filter
Low-Rank Sinkhorn Factorization
Meyer Scetbon, Marco Cuturi, Gabriel Peyré
Several recent applications of optimal transport (OT) theory to machine learning have relied on regularization, notably entropy and the Sinkhorn algorithm. Because matrix-vector pr…
Distribution-Based Invariant Deep Networks for Learning Meta-Features
Gwendoline De Bie, Herilalaina Rakotoarison, Gabriel Peyré +1
Recent advances in deep learning from probability distributions successfully achieve classification or regression from distribution samples, thus invariant under permutation of the…
Super-efficiency of automatic differentiation for functions defined as a minimum
Pierre Ablin, Gabriel Peyré, Thomas Moreau
In min-min optimization or max-min optimization, one has to compute the gradient of a function defined as a minimum. In most cases, the minimum has no closed-form, and an approxima…
Degrees of freedom for off-the-grid sparse estimation
Clarice Poon, Gabriel Peyré
A central question in modern machine learning and imaging sciences is to quantify the number of effective parameters of vastly over-parameterized models. The degrees of freedom is…
Ground Metric Learning on Graphs
Matthieu Heitz, Nicolas Bonneel, David Coeurjolly +2
Optimal transport (OT) distances between probability distributions are parameterized by the ground metric they use between observations. Their relevance for real-life applications…
GAN and VAE from an Optimal Transport Point of View
Aude Genevay, Gabriel Peyré, Marco Cuturi
This short article revisits some of the ideas introduced in arXiv:1701.07875 and arXiv:1705.07642 in a simple setup. This sheds some lights on the connexions between Variational Au…