1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2025★ 1 cited
Evidential Uncertainty Probes for Graph Neural Networks
Linlin Yu, Kangshuo Li, Pritom Kumar Saha +2
Accurate quantification of both aleatoric and epistemic uncertainties is essential when deploying Graph Neural Networks (GNNs) in high-stakes applications such as drug discovery an…
eess.SP2024
Time-Varying Graph Signal Recovery Using High-Order Smoothness and Adaptive Low-rankness
Weihong Guo, Yifei Lou, Jing Qin +1
Time-varying graph signal recovery has been widely used in many applications, including climate change, environmental hazard monitoring, and epidemic studies. It is crucial to choo…
cs.LG2023
Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization
Russell Alan Hart, Linlin Yu, Yifei Lou +1
Deep neural networks have achieved significant success in the last decades, but they are not well-calibrated and often produce unreliable predictions. A large number of literature…