117 citations · 133 across the 15 of their papers we have counts for
7 papers · 1 filter
Risk-aware Classification via Uncertainty Quantification
Murat Sensoy, Lance M. Kaplan, Simon Julier +2
Autonomous and semi-autonomous systems are using deep learning models to improve decision-making. However, deep classifiers can be overly confident in their incorrect predictions,…
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer
Marco Cominelli, Francesco Gringoli, Lance M. Kaplan +5
Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing a…
Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data
Marco Cominelli, Francesco Gringoli, Lance M. Kaplan +2
Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a pr…
FlexLoc: Conditional Neural Networks for Zero-Shot Sensor Perspective Invariance in Object Localization with Distributed Multimodal Sensors
Jason Wu, Ziqi Wang, Xiaomin Ouyang +5
Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sen…
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…