Energy based diffusion generator for efficient sampling of Boltzmann
  distributions
Yan Wang Ling Guo Hao Wu Tao Zhou
We introduce a novel sampler called the energy based diffusion generator for
generating samples from arbitrary target distributions. The sampling model
employs a structure similar to a variational autoencoder, utilizing a decoder
to transform latent variables from a simple distribution into random variables
approximating the target distribution, and we design an encoder based on the
diffusion model. Leveraging the powerful modeling capacity of the diffusion
model for complex distributions, we can obtain an accurate variational estimate
of the Kullback-Leibler divergence between the distributions of the generated
samples and the target. Moreover, we propose a decoder based on generalized
Hamiltonian dynamics to further enhance sampling performance. Through empirical
evaluation, we demonstrate the effectiveness of our method across various
complex distribution functions, showcasing its superiority compared to existing
methods.