We show that our method successful generates low energy transitions for Alanine Dipeptide as well as the larger Polyproline and Chignolin proteins.

Sampling in discrete spaces, with critical applications in simulation and optimization, has recently been boosted by significant advances in gradient-based approaches that exploit modern accelerators like GPUs. However, two key challenges are hindering further advancement in research on discrete sampling.

From a Bayesian perspective, they offer a realistic modeling of data priors and facilitate solving inverse problems through posterior sampling.

We report the details of hyperparameters in our experiments.

Generative models are being increasingly used in science and industry applications.

By Jensen'logp(x) ElogR, so logR is log-likelihood.

Mini-batch training coupled with sampling is used to alleviate this challenge.