报告人：汪扬 教授

主持人：苗俊杰 副教授

时  间：2020年10月20日下午15：30

地  点：闵行校区数学楼129  腾讯会议ID：777 286 553

报告人简介：

汪扬教授，香港科技大学副校长及数学系讲座教授，国际知名学者，研究范畴广泛；主要研究领域包括小波分析，tiling，分形几何，数字信号处理，人工智能等，在Invent. Math., Duke J. Math., Geom. and Func. Anal.等国际主要数学刊物上发表论文一百余篇，目前担任J. of Fourier Analysis and Appl.等杂志的副主编。他曾于2006至2007年间担任美国国家科学基金会的课程主任。

报告内容简介：

Abstract: Generative Adversarial Nets (GAN) have been one of the most exciting developments in machine learning and AI. But training of GAN is highly nontrivial. In this talk I will give an introduction to GAN, and propose a framework to learn deep generative models via Variational Gradient Flow (Vgrow) on probability measure spaces. Connections of our proposed VGrow method with other popular methods, such as VAE, GAN and flow-based methods, have been established in this framework, gaining new insights of deep generative learning.Abstract: Generative Adversarial Nets (GAN) have been one of the most exciting developments in machine learning and AI. But training of GAN is highly nontrivial. In this talk I will give an introduction to GAN, and propose a framework to learn deep generative models via Variational Gradient Flow (Vgrow) on probability measure spaces. Connections of our proposed VGrow method with other popular methods, such as VAE, GAN and flow-based methods, have been established in this framework, gaining new insights of deep generative learning.Generative Adversarial Nets (GAN) have been one of the most exciting developments in machine learning and AI. But training of GAN is highly nontrivial. In this talk I will give an introduction to GAN, and propose a framework to learn deep generative models via Variational Gradient Flow (Vgrow) on probability measure spaces. Connections of our proposed VGrow method with other popular methods, such as VAE, GAN and flow-based methods, have been established in this framework, gaining new insights of deep generative learning.