Stein Neural Sampler
Machine Learning
2021-02-10 v2 Machine Learning
Abstract
We propose two novel samplers to generate high-quality samples from a given (un-normalized) probability density. Motivated by the success of generative adversarial networks, we construct our samplers using deep neural networks that transform a reference distribution to the target distribution. Training schemes are developed to minimize two variations of the Stein discrepancy, which is designed to work with un-normalized densities. Once trained, our samplers are able to generate samples instantaneously. We show that the proposed methods are theoretically sound and experience fewer convergence issues compared with traditional sampling approaches according to our empirical studies.
Cite
@article{arxiv.1810.03545,
title = {Stein Neural Sampler},
author = {Tianyang Hu and Zixiang Chen and Hanxi Sun and Jincheng Bai and Mao Ye and Guang Cheng},
journal= {arXiv preprint arXiv:1810.03545},
year = {2021}
}