31 citations · 72 across the 5 of their papers we have counts for
5 papers
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification
Shiqi Wang, Huan Zhang, Kaidi Xu +4
Bound propagation based incomplete neural network verifiers such as CROWN are very efficient and can significantly accelerate branch-and-bound (BaB) based complete verification of…
HYDRA: Pruning Adversarially Robust Neural Networks
Vikash Sehwag, Shiqi Wang, Prateek Mittal +1
In safety-critical but computationally resource-constrained applications, deep learning faces two key challenges: lack of robustness against adversarial attacks and large neural ne…
Cost-Aware Robust Tree Ensembles for Security Applications
Yizheng Chen, Shiqi Wang, Weifan Jiang +2
There are various costs for attackers to manipulate the features of security classifiers. The costs are asymmetric across features and to the directions of changes, which cannot be…
On the Mathematical Understanding of ResNet with Feynman Path Integral
Minghao Yin, Xiu Li, Yongbing Zhang +1
In this paper, we aim to understand Residual Network (ResNet) in a scientifically sound way by providing a bridge between ResNet and Feynman path integral. In particular, we prove…
MixTrain: Scalable Training of Verifiably Robust Neural Networks
Shiqi Wang, Yizheng Chen, Ahmed Abdou +1
Making neural networks robust against adversarial inputs has resulted in an arms race between new defenses and attacks. The most promising defenses, adversarially robust training a…