196 citations · 408 across the 16 of their papers we have counts for
6 papers · 1 filter
Unlocking Deterministic Robustness Certification on ImageNet
Kai Hu, Andy Zou, Zifan Wang +2
Despite the promise of Lipschitz-based methods for provably-robust deep learning with deterministic guarantees, current state-of-the-art results are limited to feed-forward Convolu…
On the Perils of Cascading Robust Classifiers
Ravi Mangal, Zifan Wang, Chi Zhang +3
Ensembling certifiably robust neural networks is a promising approach for improving the \emph{certified robust accuracy} of neural models. Black-box ensembles that assume only quer…
Faithful Explanations for Deep Graph Models
Zifan Wang, Yuhang Yao, Chaoran Zhang +5
This paper studies faithful explanations for Graph Neural Networks (GNNs). First, we provide a new and general method for formally characterizing the faithfulness of explanations f…
Globally-Robust Neural Networks
Klas Leino, Zifan Wang, Matt Fredrikson
The threat of adversarial examples has motivated work on training certifiably robust neural networks to facilitate efficient verification of local robustness at inference time. We…
Smoothed Geometry for Robust Attribution
Zifan Wang, Haofan Wang, Shakul Ramkumar +3
Feature attributions are a popular tool for explaining the behavior of Deep Neural Networks (DNNs), but have recently been shown to be vulnerable to attacks that produce divergent…
Towards Frequency-Based Explanation for Robust CNN
Zifan Wang, Yilin Yang, Ankit Shrivastava +2
Current explanation techniques towards a transparent Convolutional Neural Network (CNN) mainly focuses on building connections between the human-understandable input features with…