Room 505, Cosmology Building, National Taiwan University + Cisco WebEx, Physical+Online Seminar
(實體+線上演講 台灣大學次震宇宙館505研討室+ Cisco WebEx)
Mathematical Formulation of Diffusion Models in Machine Learning
Shang-Yuan Shiu (National Central University)
Abstract
This talk offers a mathematical introduction to diffusion models in generative AI, based on their original formulations as presented in references [1] and [2], without introducing new insights. We will begin by discussing diffusion processes. Within the context of diffusion models, we will illustrate both the forward and reverse diffusion processes, highlighting their roles within the generative AI framework. The emphasis will be on the mathematical structure rather than intuitive explanations or theoretical justifications.
References
[1] Ho, J., Jain, A., and Abbeel, P. (2020). Denoising Diffusion Probabilistic Models.
[2] Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Surya Ganguli. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. In International Conference on Machine Learning, 2256–2265
Meeting number (access code): 2514 242 9124
Meeting password: HRkmwDDN822
Organizers: Jhih-Huang Li (NTU), Wai Kit Lam (NTU)
