1 citations · 1 across the 3 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
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…
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…