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Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization
A large number of literature relies on uncertainty quantification to evaluate the reliability of a learning model, which is particularly important for applications of out-of-distribution (OOD) detection and misclassification detection.
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