133 citations · 379 across the 18 of their papers we have counts for
8 papers · 1 filter
DANCE: Enhancing saliency maps using decoys
Yang Lu, Wenbo Guo, Xinyu Xing +1
Saliency methods can make deep neural network predictions more interpretable by identifying a set of critical features in an input sample, such as pixels that contribute most stron…
Explaining Deep Learning Models - A Bayesian Non-parametric Approach
Wenbo Guo, Sui Huang, Yunzhe Tao +2
Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide…
A Comparative Study of Rule Extraction for Recurrent Neural Networks
Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia +3
Understanding recurrent networks through rule extraction has a long history. This has taken on new interests due to the need for interpreting or verifying neural networks. One basi…
An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks
Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia +3
Rule extraction from black-box models is critical in domains that require model validation before implementation, as can be the case in credit scoring and medical diagnosis. Though…
Towards Interrogating Discriminative Machine Learning Models
Wenbo Guo, Kaixuan Zhang, Lin Lin +2
It is oftentimes impossible to understand how machine learning models reach a decision. While recent research has proposed various technical approaches to provide some clues as to…
Learning Adversary-Resistant Deep Neural Networks
Qinglong Wang, Wenbo Guo, Kaixuan Zhang +4
Deep neural networks (DNNs) have proven to be quite effective in a vast array of machine learning tasks, with recent examples in cyber security and autonomous vehicles. Despite the…