385 citations · 1.1k across the 45 of their papers we have counts for
48 papers
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein
Hugues Van Assel, Cédric Vincent-Cuaz, Nicolas Courty +3
Unsupervised learning aims to capture the underlying structure of potentially large and high-dimensional datasets. Traditionally, this involves using dimensionality reduction (DR)…
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein
Hugues Van Assel, Cédric Vincent-Cuaz, Titouan Vayer +2
We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes. Correspondances betwee…
Optimal Transport with Adaptive Regularisation
Hugues Van Assel, Titouan Vayer, Remi Flamary +1
Regularising the primal formulation of optimal transport (OT) with a strictly convex term leads to enhanced numerical complexity and a denser transport plan. Many formulations impo…
Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
François Painblanc, Laetitia Chapel, Nicolas Courty +3
While large volumes of unlabeled data are usually available, associated labels are often scarce. The unsupervised domain adaptation problem aims at exploiting labels from a source…
Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics
Guillaume Mahey, Laetitia Chapel, Gilles Gasso +2
Wasserstein distance (WD) and the associated optimal transport plan have been proven useful in many applications where probability measures are at stake. In this paper, we propose…
Joint multi-modal Self-Supervised pre-training in Remote Sensing: Application to Methane Source Classification
Paul Berg, Minh-Tan Pham, Nicolas Courty
With the current ubiquity of deep learning methods to solve computer vision and remote sensing specific tasks, the need for labelled data is growing constantly. However, in many ca…