Deterministic Sampling

Deterministic sampling aims to generate samples from a probability distribution using deterministic algorithms, offering an alternative to stochastic methods. Current research focuses on improving the efficiency and accuracy of these deterministic samplers, particularly within the context of score-based generative models and diffusion processes, employing techniques like probability flow ODEs and adaptive kernel methods. These advancements are impacting various fields, including image generation, 3D modeling, and code optimization, by providing faster and more controlled sampling procedures compared to traditional stochastic approaches. The development of provably convergent algorithms and the exploration of connections to established frameworks like quantum physics are key themes driving progress.