报告人：苏炜杰（宾夕法尼亚大学沃顿商学院）

报告时间：2024年7月9日16:00-17:00

报告地点：海纳苑2幢106

摘要: In this talk, we introduce a universal phenomenon that governs the inner workings of a wide range of neural network architectures, including multilayer perceptrons, convolutional neural networks, transformers, and Mamba. Through extensive computational experiments, we demonstrate that deep neural networks tend to process data in a uniform improvement manner across layers. We conclude this talk by discussing how this universal law provides useful insights into practice.

联系人：赖俊（laijun6@zju.edu.cn）