Understanding Machine Learning from Partial Differential Equations
计算数学专题报告
报告题目(Title)：Understanding Machine Learning from Partial Differential Equations
报告人(Speaker)：史作强（清华大学）
地点(Place)：教八114
时间(Time)：2019年4月17日15:30-16:20
邀请人(Inviter)：刘君
报告摘要
In this talk, I will present several PDE models and show their relations to machine learning and deep learning problem. In these PDE models, we use manifold to model the low dimensional structure hidden in high dimensional data and use PDEs to study the manifold. I will reveal the close connections between PDEs and deep neural networks. Theoretical analysis and numerical simulations show that PDEs provide us powerful tools to understand high dimensional data.
主讲人简介
史作强，清华大学丘成桐数学科学中心，数学科学系副教授，主要研究方向为偏微分方程数值方法，图像处理和机器学习中的偏微分方程模型，非线性非平稳信号时频分析等，共发表文章30余篇。
