WebThis repository contains an implementation of the Wasserstein gradient flow of the Maximum Mean Discrepancy from Maxmimum Mean Discrepancy Gradient Flow … Webshow that gradient descent on the parameters of a neural network can also be seen as a particle transport problem, which has as its population limit a gradient flow of a functional

[1906.04370] Maximum Mean Discrepancy Gradient Flow - arXiv.org

WebMaximum Mean Discrepancy (MMD) [4] and the Kernelized Sobolev Discrepancy (KSD) [45,44]. One motivation for considering these particle flows is their connection with the training of Generative Adversarial Networks (GANs) [28] using IPMs such as the Wasserstein distance [7, 29, 27], the MMD [24, 34, 33, 9, 10, 5] or the Sobolev … Webkernels by maximizing the Maximum Mean Discrepancy (MMD), which is not suitable when there is only one distribution involved, e.g., learning the parameter manifold in Bayesian Inference. 2 Preliminaries 2.1 Riemannian Manifold We use Mto denote manifold, and dim(M) to denote the dimensionality of manifold M. We chuck\u0027s richmond indiana

[2301.11624] Neural Wasserstein Gradient Flows for Maximum Mean ...

WebWe consider the maximum mean discrepancy MMD GAN problem and propose a parametric kernelized gradient flow that mimics the min-max game in gradient … Web- "Maximum Mean Discrepancy Gradient Flow" Figure 2: Gradient flow of the MMD from a gaussian initial distributions ν0 ∼ N (10, 0.5) towards a target distribution µ ∼ N (0, 1) … WebMaximum Mean Discrepancy Gradient Flow Reviewer 1 This paper seems to accomplish two feats at once: it provides a rather deep dive into the specific topic of gradient flows … chuck\\u0027s ride