Discrete Diffusion with Sample-Efficient Estimators for Conditionals
Researchers introduce a discrete denoising diffusion framework that leverages a sample-efficient estimator (NeurISE) for single-site conditional probabilities, eliminating the need for score function approximations. Experiments on binary datasets, including Ising models, MNIST, and quantum annealer data, show that this method outperforms existing approaches in several evaluation metrics.
Why it matters: This work presents a more sample-efficient approach to discrete diffusion, potentially advancing generative modeling for scientific and binary data.
Full story at: arXiv Statistical ML ↗