385 citations · 1.1k across the 45 of their papers we have counts for
6 papers · 1 filter
Inter-generational comparison of quantum annealers in solving hard scheduling problems
Bibek Pokharel, Zoe Gonzalez Izquierdo, P. Aaron Lott +6
We compare the performance of four quantum annealers, the D-Wave Two, 2X, 2000Q, and Advantage in solving an identical ensemble of a parametrized family of scheduling problems. The…
Semi-relaxed Gromov-Wasserstein divergence with applications on graphs
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli +2
Comparing structured objects such as graphs is a fundamental operation involved in many learning tasks. To this end, the Gromov-Wasserstein (GW) distance, based on Optimal Transpor…
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
Kilian Fatras, Thibault Séjourné, Nicolas Courty +1
Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions. Yet their algorithmic complexit…
Learning to Generate Wasserstein Barycenters
Julien Lacombe, Julie Digne, Nicolas Courty +1
Optimal transport is a notoriously difficult problem to solve numerically, with current approaches often remaining intractable for very large scale applications such as those encou…
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…
Minibatch optimal transport distances; analysis and applications
Kilian Fatras, Younes Zine, Szymon Majewski +3
Optimal transport distances have become a classic tool to compare probability distributions and have found many applications in machine learning. Yet, despite recent algorithmic de…