报告人：邓波（内布拉斯加大学林肯分校）

时间：2024年06月28日 16:20-17:20

地址：理科楼LA103

摘要：Models of Artificial Neural Networks play an essential role in Artificial Intelligence.All ANN models must be trained before they are deployed to perform tasks. The majority of AI training is supervised. For large-scale models, there are no known methods to achieve 100% accuracy for supervised training. In this talk, I will discuss a newly discovered method that can train ANN models to perfect precision. I will outline the ideas from Dynamical Systems that guarantee the convergence of the error-free training algorithm, and show simulations on the most popular benchmark data for training algorithms in the field. I will also discuss the relationship between the ANN training problem and the classification problem of finite points in Euclidean space that is based on the Stone–Weierstrass approximation theorem in Analysis.

简介：邓波，内布拉斯加大学林肯分校数学系教授。1977年应届毕业生考进复旦数学系，1981年考取教育部留美研究生，1987年获MSU博士学位，师从周修义教授。1987 －1988在布朗大学跟Jack Hale做博士后。1988年入职内布拉斯加大学林肯分校至今。研究领域是动力系统、生物数学和人工智能。

邀请人：穆春来

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