4月21日
研討會, 演講, 講座
MATH - PhD Student Seminar - Approximation and generalization bounds for generative adversarial networks
The remarkable empirical performance of Generative Adversarial Networks (GANs) in generating high-quality samples have attracted enormous attention in the past few years. In this talk, we discuss how well can GANs approximate and learn high-dimensional distributions.