Oct 8, 2018 · Abstract:We propose two novel samplers to generate high-quality samples from a given (un-normalized) probability density.
Feb 9, 2021 · In this paper, we propose two novel sampling schemes based on Stein discrepancy that can directly learn preservable transformations constructed ...
Once trained, our samplers are able to generate samples instantaneously. We show that the proposed methods are theoretically sound and experience fewer ...
Two novel samplers to produce high-quality samples from a given (un-normalized) probability density are proposed by transforming a reference distribution to ...
This repository contains the code for the paper Stein Neural Sampler. We implemented the KSD Neural Sampler (with the RBF or IMQ kernels) and the Fisher ...
We propose a simple algorithm to train stochas- tic neural networks to draw samples from given target distributions for probabilistic inference.
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Stein Neural Sampler ... We propose two novel samplers to generate high-quality samples from a given (un-normalized) probability density. ... Cannot find the paper ...
Jul 8, 2019 · We propose two novel sampling methods to generate high-quality samples from a given (un-normalized) probability density. Motivated by the ...
Jul 27, 2023 · In this paper, we investigate the role of L^{2} regularization in training a neural network Stein critic so as to distinguish between data sampled from an ...
We show that sparsification prior to Stein variational gradient descent ( +SVGD) is a more robust and efficient means of uncertainty quantification.