Deep Approximation via Deep Learning

主讲人 Speaker:沈佐伟
时间 Time: Fri. 4:00-5:00 pm,2021-12-3
地点 Venue:Zoom Meeting ID:849 963 1368 Passcode:YMSC

摘要 Abstract

The primary task of many applications is approximating/estimating  a function  through  samples drawn from a probability distribution on the input space.  The deep approximation  is to  approximate  a function by compositions of many layers of simple functions, that can be viewed as  a series of nested feature extractors. The  key idea of deep learning  network is to convert layers of compositions to  layers of tuneable parameters that  can be adjusted through a  learning process,  so that it achieves a good approximation with respect to the input data.  In this talk, we  shall discuss mathematical theory behind  this new approach and approximation rate of deep network;   we will also show  how  this new approach  differs from  the classic approximation theory, and  how this new theory can be used to understand and design  deep learning network.

报告人介绍 Profile

沈佐伟,新加坡国立大学陈振传百年纪念教授,主要研究方向是数据科学中的数学理论及其应用。研究领域包括逼近与小波理论、图像科学、压缩感知及机器学习等。作为国际著名数学家,沈佐伟教授先后受邀在2010年国际数学家大会和2015年国际工业与应用数学大会上作报告。沈佐伟教授是新加坡国家科学院院士,发展中国家科学院院士,美国数学会会士(AMS Fellow),美国工业与应用数学会会士(SIAM Fellow)。