Diffusion Sampler

Diffusion samplers are generative models that create new data instances by reversing a noise-diffusion process, aiming to efficiently generate samples from complex probability distributions. Current research focuses on improving sampling speed and accuracy through novel algorithms like stochastic Runge-Kutta methods and Boltzmann samplers, as well as refining existing methods such as probability flow ODE samplers and rejection sampling techniques. These advancements are impacting diverse fields, including molecular dynamics simulations, image generation, and solving combinatorial optimization problems, by providing more efficient and accurate sampling methods for high-dimensional data.