Title: Generative Adversarial Networks (GANs): An Analytical Perspective
Abstract: Generative Adversarial Networks (GANs) have attracted intense interest in computer vision and image generation. In this talk, I will first provide a gentle review of the mathematical framework behind GANs. I will then proceed to discuss a few challenges in GANs training from an analytical perspective. I will finally report some recent progress for GANs training in terms of its stability and convergence analysis.
Bio: Xin Guo is the Coleman Fung Chair professor at the college of engineering, UC Berkeley. Prior to that, she was an associate professor at the School of ORIE, Cornell University, and a research staff member at the Mathematics Department of IBM T.J. Watson Research Center. Her research interests are in stochastic processes, stochastic controls and games, and mathematics of machine learning, with applications to financial engineering and medical data analysis. She is the co-editor-in-chief for Frontier of Mathematical Finance, and on the editorial board of several leading journals of controls, applied probability, mathematical finance, and operation research.
