Stein Neural Sampler
This work addresses sampling challenges in machine learning, offering a more efficient alternative for practitioners, though it appears incremental as it builds on existing generative adversarial network ideas.
The authors tackled the problem of generating high-quality samples from un-normalized probability densities by proposing two novel samplers based on deep neural networks, which minimize Stein discrepancy variations and produce samples instantaneously with fewer convergence issues compared to traditional methods.
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.