A Dynamical System Perspective for Lipschitz Neural Networks
组织者
丘成栋
演讲者
康家熠
时间
2022年12月13日 21:30 至 22:00
地点
Online
摘要
I shall report a paper on Lipschitz Neural Networks. The Lipschitz constant of neural networks has been established as a key quantity to enforce the robustness to adversarial examples. In this paper, we tackle the problem of building 1-Lipschitz Neural Networks. By studying Residual Networks from a continuous time dynamical system perspective, we provide a generic method to build 1-Lipschitz Neural Networks and show that some previous approaches are special cases of this framework. Then, we extend this reasoning and show that ResNet flows derived from convex potentials define 1-Lipschitz transformations, that lead us to define the Convex Potential Layer (CPL). A comprehensive set of experiments on several datasets demonstrates the scalability of our architecture and the benefits as a 2-provable defense against adversarial examples.
演讲者介绍
Jiayi Kang received his Ph.D. in Mathematics from Tsinghua University in 2024. He joined the Beijing Institute of Mathematical Sciences and Applications (BIMSA) as an Assistant Researcher in July 2024, and became an Assistant Professor at the Hetao Institute for Mathematical and Interdisciplinary Sciences (HIMIS) in November 2025.
His research focuses on the intersection of deep learning, nonlinear filtering, and computational biology. His main research interests include: neural network-based filtering algorithms and their mathematical foundations, sampling methods in Wasserstein geometry, nonlinear filtering theory (including the Yau-Yau method) and its applications in climate science and other fields, as well as computational genomics and evolutionary system modeling. He is committed to solving complex problems in science and engineering using mathematical and machine learning methods.