Gradient-guided discrete walk-jump sampling for biological sequence generation
Abstract: In this work, we propose gradient-guided discrete walk-jump sampling (gg-dWJS), a novel discrete sequence generation method for biological sequence optimization. Leveraging gradient guidance in the noisy manifold, we sample from the smoothed data manifold by applying discretized Markov chain Monte Carlo (MCMC) using a denoising model with the gradient-guidance from a discriminative model. This is followed by jumping to the discrete data manifold using a conditional one-step denoising. We showcase our method in two different
modalities: discrete image and antibody sequence generation tasks in the single objective and multi-objective setting. Through evaluation on these tasks, we show that our method generates high-quality samples that are well-optimized for specific tasks.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Patrick_Flaherty1
Submission Number: 3388
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