  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.
