7 citations · 13 across the 4 of their papers we have counts for
4 papers
Consistent Counterfactuals for Deep Models
Emily Black, Zifan Wang, Matt Fredrikson +1
Counterfactual examples are one of the most commonly-cited methods for explaining the predictions of machine learning models in key areas such as finance and medical diagnosis. Cou…
Robust Models Are More Interpretable Because Attributions Look Normal
Zifan Wang, Matt Fredrikson, Anupam Datta
Recent work has found that adversarially-robust deep networks used for image classification are more interpretable: their feature attributions tend to be sharper, and are more conc…
Influence Patterns for Explaining Information Flow in BERT
Kaiji Lu, Zifan Wang, Piotr Mardziel +1
While attention is all you need may be proving true, we do not know why: attention-based transformer models such as BERT are superior but how information flows from input tokens to…
Reconstructing Actions To Explain Deep Reinforcement Learning
Xuan Chen, Zifan Wang, Yucai Fan +4
Feature attribution has been a foundational building block for explaining the input feature importance in supervised learning with Deep Neural Network (DNNs), but face new challeng…