Protein Discovery with Discrete Walk-Jump Sampling

arXiv:2306.12360v2 Announce Type: replace-cross
Abstract: We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising. Our Discrete Walk-Jump Sampling formalism combines the contrastive divergence training of an energy-based model and improved sample quality of a score-based model, while simplifying training and sampling by requiring only a single noise …

abstract arxiv cs.lg data denoising discovery energy function generative manifold markov mcmc protein q-bio.bm sampling training true type