6 citations · 8 across the 4 of their papers we have counts for
5 papers
A Tale of Two Approximations: Tightening Over-Approximation for DNN Robustness Verification via Under-Approximation
Zhiyi Xue, Si Liu, Zhaodi Zhang +2
The robustness of deep neural networks (DNNs) is crucial to the hosting system's reliability and security. Formal verification has been demonstrated to be effective in providing pr…
Boosting Verified Training for Robust Image Classifications via Abstraction
Zhaodi Zhang, Zhiyi Xue, Yang Chen +4
This paper proposes a novel, abstraction-based, certified training method for robust image classifiers. Via abstraction, all perturbed images are mapped into intervals before feedi…
Efficient Black-box Checking of Snapshot Isolation in Databases
Kaile Huang, Si Liu, Zhenge Chen +4
Snapshot isolation (SI) is a prevalent weak isolation level that avoids the performance penalty imposed by serializability and simultaneously prevents various undesired data anomal…
DualApp: Tight Over-Approximation for Neural Network Robustness Verification via Under-Approximation
Yiting Wu, Zhaodi Zhang, Zhiyi Xue +2
The robustness of neural networks is fundamental to the hosting system's reliability and security. Formal verification has been proven to be effective in providing provable robustn…
Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training
Jiaxu Tian, Dapeng Zhi, Si Liu +3
The intrinsic complexity of deep neural networks (DNNs) makes it challenging to verify not only the networks themselves but also the hosting DNN-controlled systems. Reachability an…