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stat.ML2023
Gromov-Wasserstein-like Distances in the Gaussian Mixture Models Space
Antoine Salmona, Julie Delon, Agnès Desolneux
The Gromov-Wasserstein (GW) distance is frequently used in machine learning to compare distributions across distinct metric spaces. Despite its utility, it remains computationally…
stat.ML2023
Properties of Discrete Sliced Wasserstein Losses
Eloi Tanguy, Rémi Flamary, Julie Delon
The Sliced Wasserstein (SW) distance has become a popular alternative to the Wasserstein distance for comparing probability measures. Widespread applications include image processi…