Uncertainty-Aware Learning

Framework of the proposed IB-D2GAT

IB-D2GAT: How Information Bottleneck Theory Revolutionizes Dynamic Graph Learning Under Distribution Shifts

Leave a Comment / Machine Learning /

Introduction: The Critical Challenge of Evolving Graph Data In an era where financial transactions occur in milliseconds, social networks reshape human interaction by the minute, and traffic patterns shift with unpredictable urban dynamics, dynamic graph neural networks (DyGNNs) have emerged as essential tools for modeling real-world systems. Unlike static graphs that capture frozen snapshots of […]

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Uncertainty Beats Confidence in semi-supervised learning

1 Comment / Machine Learning /

In the ever-evolving landscape of artificial intelligence, semi-supervised learning (SSL) has emerged as a powerful approach for harnessing the vast potential of unlabeled data. Traditionally, SSL techniques rely heavily on pseudo-labels—model-generated labels for unlabeled samples—and confidence thresholds to determine their reliability. But this paradigm has long suffered from a critical flaw: overconfidence in model predictions

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