76 citations · 313 across the 7 of their papers we have counts for
7 papers
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images
Bharath Bhushan Damodaran, Rémi Flamary, Viven Seguy +1
Deep neural networks have established as a powerful tool for large scale supervised classification tasks. The state-of-the-art performances of deep neural networks are conditioned…
Fast flow-based algorithm for creating density-equalizing map projections
Michael T. Gastner, Vivien Seguy, Pratyush More
Cartograms are maps that rescale geographic regions (e.g., countries, districts) such that their areas are proportional to quantitative demographic data (e.g., population size, gro…
Blind Source Separation with Optimal Transport Non-negative Matrix Factorization
Antoine Rolet, Vivien Seguy, Mathieu Blondel +1
Optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention. Building upon recent advances in computational optimal transport, we…
Large-Scale Optimal Transport and Mapping Estimation
Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary +3
This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) pla…
Smooth and Sparse Optimal Transport
Mathieu Blondel, Vivien Seguy, Antoine Rolet
Entropic regularization is quickly emerging as a new standard in optimal transport (OT). It enables to cast the OT computation as a differentiable and unconstrained convex optimiza…
Log-PCA versus Geodesic PCA of histograms in the Wasserstein space
Elsa Cazelles, Vivien Seguy, Jérémie Bigot +2
This paper is concerned by the statistical analysis of data sets whose elements are random histograms. For the purpose of learning principal modes of variation from such data, we c…