activity
20192022
most citedTowards Frequency-Based Explanation for Robust CNN

24 citations · 30 across the 3 of their papers we have counts for

collaborators

7 papers

cs.LG2022

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…

eess.SY20226 cited

On Optimizing Shared-ride Mobility Services with Walking Legs

Zifan Wang, Michael F Hyland, Younghun Bahk +1

Shared-ride mobility services that incorporate traveler walking legs aim to reduce vehicle-kilometers-travelled (VKT), vehicle-hours-travelled (VHT), request rejections, fleet size…

cs.LG2021

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…

cs.LG2020

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…

cs.LG202024 cited

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…

cs.AI2020

Interpreting Interpretations: Organizing Attribution Methods by Criteria

Zifan Wang, Piotr Mardziel, Anupam Datta +1

Motivated by distinct, though related, criteria, a growing number of attribution methods have been developed tointerprete deep learning. While each relies on the interpretability o…