362 citations · 474 across the 19 of their papers we have counts for
19 papers
Visualizing Uncertainty: Spatial Maps of Missing and Conflicting Evidence in Deep Learning
Dong Hyun Jeong, Feng Chen, Jin-Hee Cho +3
Understanding when and why deep neural networks are uncertain is crucial for deploying reliable machine learning systems in safety-critical domains. While existing uncertainty quan…
X-MAP: eXplainable Misclassification Analysis and Profiling for Spam and Phishing Detection
Qi Zhang, Dian Chen, Lance M. Kaplan +4
Misclassifications in spam and phishing detection are very harmful, as false negatives expose users to attacks while false positives degrade trust. Existing uncertainty-based detec…
Beyond Binary Opinions: A Deep Reinforcement Learning-Based Approach to Uncertainty-Aware Competitive Influence Maximization
Qi Zhang, Dian Chen, Lance M. Kaplan +4
The Competitive Influence Maximization (CIM) problem involves multiple entities competing for influence in online social networks (OSNs). While Deep Reinforcement Learning (DRL) ha…
Winning the Social Media Influence Battle: Uncertainty-Aware Opinions to Understand and Spread True Information via Competitive Influence Maximization
Qi Zhang, Lance M. Kaplan, Audun Jøsang +3
Competitive Influence Maximization (CIM) involves entities competing to maximize influence in online social networks (OSNs). Current Deep Reinforcement Learning (DRL) methods in CI…
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
Changbin Li, Kangshuo Li, Yuzhe Ou +5
Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes…
TIPS: Threat Sharing Information Platform for Enhanced Security
Lakshmi Rama Kiran Pasumarthy, Hisham Ali, William J Buchanan +4
There is an increasing need to share threat information for the prevention of widespread cyber-attacks. While threat-related information sharing can be conducted through traditiona…