题目：Error-free Training for Artificial Neural Networks
报告人：Bo Deng, 内布拉斯加大学林肯分校, 教授
时间: 06月27日（周四）上午10: 30-11: 20
地点：X30456
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.
个人简介：邓波，八一年学士学位：复旦大学数学系七七届。八七年博士学位：密执安州立大学应用数学。博士后：1987-1988，布朗大学。现任内布拉斯加大学林肯分校数学系教授。主要学术研究领域：动力系统，生物数学，人工智能。
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