6 citations · 9 across the 2 of their papers we have counts for
7 papers
Template based Graph Neural Network with Optimal Transport Distances
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli +2
Current Graph Neural Networks (GNN) architectures generally rely on two important components: node features embedding through message passing, and aggregation with a specialized fo…
Fast Multiscale Diffusion on Graphs
Sibylle Marcotte, Amélie Barbe, Rémi Gribonval +4
Diffusing a graph signal at multiple scales requires computing the action of the exponential of several multiples of the Laplacian matrix. We tighten a bound on the approximation e…
Online Graph Dictionary Learning
Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary +2
Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements. Yet, this analysis is not amenable in the context…
A contribution to Optimal Transport on incomparable spaces
Titouan Vayer
Optimal Transport is a theory that allows to define geometrical notions of distance between probability distributions and to find correspondences, relationships, between sets of po…
CO-Optimal Transport
Ievgen Redko, Titouan Vayer, Rémi Flamary +1
Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions. Yet, its original formulation…
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties
Titouan Vayer, Laetita Chapel, Rémi Flamary +2
Optimal transport theory has recently found many applications in machine learning thanks to its capacity for comparing various machine learning objects considered as distributions.…