The Sinkhorn loss can be computed for batches of a million samples. Practical side, we detail a numerical scheme that enables the large scaleĪpplication of these divergences for machine learning: on the GPU, gradients of Positivity, convexity and metrization of the convergence in law. As the name suggests, Microsoft manages the systems. Geometric entropy, we provide theoretical guarantees for these divergences: Microsoft Managed Desktop (MMD) is a subscription-based desktop as a service (DaaS) cloud platform that includes Windows 10 Enterprise, Office 365, Enterprise Mobility and Security on select Windows PCs and Windows 10-enabled devices. This paper studies the Sinkhorn divergences, a family of geometricĭivergences that interpolates between MMD and OT. Underlying space and metrize the convergence in law. In sharp contrast, Maximum Meanĭiscrepancies (MMD) and Optimal Transport distances (OT) are two classes ofĭistances between measures that take into account the geometry of the Relative entropy only compare densities in a point-wise manner and fail toĬapture the geometric nature of the problem.

Simple norms and divergences such as the total variation and the

Authors: Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouvé, Gabriel Peyré Download PDF Abstract: Comparing probability distributions is a fundamental problem in data

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