一、题目

Uncertainty Quantification in Deep Learning through Stochastic Maximum Principle

二、主讲人

Yanzhao Cao

三、摘要

We develop a probabilistic machine learning method, which formulates a class of stochastic neural networks by a stochastic optimal control problem. An efficient stochastic gradient descent algorithm is introduced under the stochastic maximum principle framework. Convergence analysis for stochastic gradient descent optimization and numerical experiments for applications of stochastic neural networks are carried out to validate our methodology in both theory and performance.

四、主讲人简介

Professor Yanzhao Cao is currently the Don Logan Endowed Chair in Mathematics at Auburn University. He received both his BA and Master degrees in mathematics from Jilin University (1983, 1986), and PhD in mathematics from Virginia Tech (1996). He is an associate editor of SIAM Journal in Numerical Analysis and Journal of Integral Equations and Applications, and a managing editor of Communications in Mathematical Research. He is also serving as the President of SIAM Southeast and Atlantic Section. His expertise is in numerical methods for partial differential equations and stochastic computing.

五、邀请人

赵卫东 数学学院教授

六、时间

12月5日（周六）9:30-10:30

七、地点

腾讯会议，会议ID：315 433 872

https://meeting.tencent.com/s/lfPkUiwcO1BY

八、主办方

山东大学数学学院