讲座题目：Deep Approximation via Deep Learing 主讲人👨👦：沈佐伟 教授 主持人：沈超敏 副教授 开始时间🦠：2019-07-06 15:00:00 结束时间⏫：2019-07-06 16:00:00 讲座地址👨🏽🦰：中北校区理科大楼B1002室 主办单位：计算机科学与软件工程学院
报告人简介： 沈佐伟教授，新加坡国立大学理学院院长🚣🏽，陈振传百年纪念教授，新加坡国家科学院院士。主要研究领域为逼近与小波理论🏦、时频分析🦸🏿、图像科学等👨🏿🌾🧛♂️。沈佐伟教授在加拿大阿尔伯塔大学获博士学位，是美国数学会会士（AMS Fellow）👨🏻⚕️、美国工业与应用数学会会士（SIAM Fellow）。作为国际著名数学家★，沈佐伟教授先后获得Wavelet Pioneer奖、新加坡国立大学杰出科学研究奖和新加坡科学成就奖，并受邀在2010年国际数学家大会上作45分钟报告。 报告内容： 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 tunable 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 foundation behind this new approach of approximation; how it differs from the classic approximation theory, and how this new theory can be applied to understand and design deep learning network. |
