Core Concepts
This paper introduces a novel framework for analyzing the error of discrete diffusion models, drawing parallels to continuous diffusion models by employing a stochastic integral approach based on Poisson random measures.
Ren, Y., Chen, H., Rotskoff, G. M., & Ying, L. (2024). How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework. arXiv preprint arXiv:2410.03601v1.
This paper aims to establish a comprehensive framework for analyzing the error of discrete diffusion models, a rapidly developing area in machine learning, by leveraging tools from stochastic analysis, particularly Poisson random measures.