CMU Artificial Intelligence Seminar Series sponsored by
Tuesday, April 5, 2022
Time: 12:00 - 01:00 PM ET
Chao Wang -- Differential Verification of Deep Neural Networks
Relevant Paper(s):
- N/A
Abstract:
Deep neural networks have become an integral component of many systems for which ensuring safety and robustness is crucial. In this talk, we present several abstract interpretation based methods for efficient verification of a class of safety properties called differential properties. While we focus on neural network equivalence as the canonical example, other interesting properties concerning input sensitivity and stability can also be cast as differential properties. Our key insight is in deriving sound abstractions that relate the intermediate computations of two structurally-similar neural networks, to accurately bound their maximum difference over all inputs. We also propose bound synthesis techniques for automatically generating linear abstractions of arbitrary nonlinear functions, to more efficiently handle architectures and activation functions beyond feed-forward ReLU networks.
Bio: Chao Wang is an Associate Professor of Computer Science at the University of Southern California (USC). He develops formal verification and program synthesis techniques for principled design of systems to improve safety and security. He has published two books and more than 100 papers. The awards and recognition he received include a Young Investigator award from the U.S. Office of Naval Research (ONR), a CAREER award from the National Science Foundation (NSF), two ACM SIGSOFT Distinguished Paper awards, and a Best Journal Paper of the Year award from ACM Transactions on Design Automation of Electronic Systems.