ICLR 2026 - Submissions

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Quantity AI Content Count Avg Rating
0-10% 1 (100%) 4.50
10-30% 0 (0%) N/A
30-50% 0 (0%) N/A
50-70% 0 (0%) N/A
70-90% 0 (0%) N/A
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Total 1 (100%) 4.50
Title Abstract Avg Rating Quantity AI Content Reviews Pangram Dashboard
Credal Graph Neural Networks for Robust Uncertainty Quantification Uncertainty quantification is essential for deploying reliable Graph Neural Networks (GNNs), where existing approaches primarily rely on Bayesian inference or ensembles. In this paper, we introduce th... 4.50 8% See Reviews View AI Dashboard
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