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About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Staff
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
Tour
News
News
Announcement
Downloads
Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > Seminar on Control Theory and Nonlinear Filtering A Dynamical System Perspective for Lipschitz Neural Networks
A Dynamical System Perspective for Lipschitz Neural Networks
Organizer
Shing Toung Yau
Speaker
Jia Yi Kang
Time
Tuesday, December 13, 2022 9:30 PM - 10:00 PM
Venue
Online
Abstract
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.
Beijing Institute of Mathematical Sciences and Applications
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