Interpretable structural temperature scaling for calibrating graph neural networks and point-cloud segmentation
Read the original at arxiv.org→arXiv:2611.00175v1 Announce Type: new Abstract: Graph-aware calibrators make graph neural networks (GNNs) more trustworthy by predicting a temperature for each node, usually with auxiliary networks whose decisions...
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- Nov 3, 05:00 UTC arXiv cs.LG lead source Interpretable structural temperature scaling for calibrating graph neural networks and point-cloud segmentation