Distinguished Colloquium——Deep Approximation via Deep Learning
报告人：Zuowei Shen (National University of Singapore)
时间：2021-11-19 15:00-16:00
地点：Online (Zoom Meeting)
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 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.
Bio: 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）。
讲座回放（Only for PKU ID）观看链接：
http://media.lib.pku.edu.cn/index.php?m=content&c=index&a=show&catid=33&id=8354
