时间：
6月8日下午1：30-15:00
地点：教学楼B202
报告人：Xianfeng Gu （State University of New York at Stony Brook)
题目：Optimal Mass Transportation and Deep Learning
摘要：The fundamental principle behind generative adversarial networks (GANs) is to manipulate probability measures, such as to transform distributions, measure the Wasserstein distance between distributions and so on. Optimal mass transportation theory offers a geometric framework to handle probability measures, which gives a unique point of view of interpreting GAN models. In this talk, the connection between Optimal mass transportation theory and the convex geometry will be discussed, a variational approach will be given which leads to the solution to the classical Monge-Ampere equation and the Wasserstein distance between distributions. The similarities between these computational approach and GAN model will be analyzed.
报告人简介：
顾险峰，清华大学计算机系学士，哈佛大学博士，师承国际著名数学大师丘成桐先生。现为美国纽约州立大学石溪分校计算机系终身教授，曾获美国NSFCAREER奖，中国海外杰青，“华人菲尔兹奖”-晨兴应用数学金奖等。顾险峰教授团队将微分几何、代数拓扑、黎曼面理论，偏微分方程与计算机科学相结合，创立跨领域学科“计算共形几何”，并广泛应用于计算机图形学，计算机视觉，三维几何建模与可视化，无线传感网络，医学图像等领域。
