SLAI Seminar 27th Session will be discussing the topic on "Mathematical foundations of generative diffusion models",  from 2:30pm to 4pm, March 5th (Thursday) at Room B401, online participation is welcome 

(Tencent Meeting ID: 946-581-613)

About the Speaker:

N. Puchkin received his B.Sc. and M.Sc. (Hons.) degrees in applied mathematics and physics from the Moscow Institute of Physics and Technology in 2016 and 2018, respectively, and an additional M.Sc. (Hons.) degree in mathematics and computer science from the Skolkovo Institute of Science and Technology in 2018. In 2023, he obtained a Ph.D. in mathematics from HSE University under the supervision of Prof. Dr. V. Spokoiny. He was awarded the 2020 Young Russian Mathematics Award and the 2024 Junior Leader Grant (mathematics). Since September 2024, he has been leading the Laboratory for Theoretical Foundations of AI Models at HSE University.

Abstract: 

Generative diffusion models have emerged as a powerful paradigm for data synthesis based on non‑equilibrium thermodynamics. They have allowed us to take a significant step towards generating high‑quality images and have also given rise to state‑of‑the‑art and emerging methods, such as flow matching and diffusion Schrödinger bridges. In my talk, I will discuss the mathematical foundations of denoising diffusion probabilistic models (DDPM) and score‑based generative models, with particular attention to both the estimation and inference phases. The main focus will be on comparing SDE‑ and ODE‑based sampling schemes and on the complexity of score estimation.