Speaker: Wenpin Tang (Assistant Professor, Columbia University)

 

 

Abstract: The past decade has witnessed the success of generative modeling (e.g. GANs, VAEs,...) in creating high quality samples in a wide variety of data modalities. The first part of this talk is concerned with the recently developed diffusion models, the key idea of which is to reverse a certain stochastic dynamics. I will first take a continuous-time perspective, and examine the performance of different SDE schemes including VE (variance exploding) and VP (variance preserving). The discretization is more subtle, and our idea is to "contract" the reversed dynamics leading to possible new diffusion model designs. In the second part, I will focus on the fine-tuning technique including guidance and RL. The discussion will be based on various probabilistic and analytical tools. 

 

 

Host: Yuming Paul Zhang