409 citations · 677 across the 54 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
cs.LG2019★ 4 cited
Searching for Stage-wise Neural Graphs In the Limit
Xin Zhou, Dejing Dou, Boyang Li
Search space is a key consideration for neural architecture search. Recently, Xie et al. (2019) found that randomly generated networks from the same distribution perform similarly,…
cs.LG2019★ 2 cited
An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness
Adam Noack, Isaac Ahern, Dejing Dou +1
Deep neural networks (DNNs) have had many successes, but they suffer from two major issues: (1) a vulnerability to adversarial examples and (2) a tendency to elude human interpreta…
cs.LG2019★ 37 cited
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks
Isaac Ahern, Adam Noack, Luis Guzman-Nateras +3
The problem of explaining deep learning models, and model predictions generally, has attracted intensive interest recently. Many successful approaches forgo global approximations i…