However,GNNs have notconsidered different types ofuncertainties associated with class probabilities to minimize risk of increasing misclassification under uncertainty in real life. In this work, we propose a multi-source uncertainty framework using a GNN that reflects various types of predictive uncertainties in both deep learning and belief/evidence theory domains fornodeclassification predictions.

Let us revisit the diffusion model in the context of image generation.

BERT is incapable of processing long texts due to its quadratically increasing memory andtimeconsumption. Themost natural waystoaddress thisproblem, such as slicing the text by a sliding window or simplifying transformers, suffer from insufficient long-range attentions orneed customized CUDAkernels.

In adversarial machine learning, deep neural networks can fit the adversarial examples on the training dataset but have poor generalizationability on the test set.