Web21 nov. 2024 · We construct Wasserstein gradient flows on two measures of divergence, and study their convergence properties. The first divergence measure is the Maximum Mean Discrepancy (MMD): an integral probability metric defined for a reproducing kernel Hilbert space (RKHS), which serves as a metric on probability measures for a sufficiently … Web28 feb. 2024 · We first verify that GSPMs are metrics. Then, we identify a subset of GSPMs that are equivalent to maximum mean discrepancy (MMD) with novel positive definite kernels, which come with a unique geometric interpretation.
Maximum Mean Discrepancy Gradient Flow OpenReview
WebWe construct a Wasserstein gradient flow of the maximum mean discrepancy (MMD) and study its convergence properties. The MMD is an integral probability metric defined for a … Web27 jan. 2024 · Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals with non-smooth Riesz kernels show a rich structure as singular measures can become absolutely continuous ones and conversely. In this paper we contribute to the understanding of such flows. We propose to approximate the backward scheme of … scotch tails borough market
Generalized Sliced Probability Metrics - IEEE Xplore
Webgradient flows) I This work: Minimize the Maximum Mean Discrepancy (MMD) on the space of probability distributions. Application : Insights on the theoretical properties of … Web11 sep. 2024 · Araújo D, Oliveira R I, Yukimura D. A mean-field limit for certain deep neural networks. arXiv:1906.00193, 2024. Arbel M, Korba A, Salim A, et al. Maximum mean … Web21 sep. 2024 · Speaker: Anna KorbaEvent: Second Symposium on Machine Learning and Dynamical Systemshttp://www.fields.utoronto.ca/activities/20-21/dynamicalTitle: … scotch tade offense