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cs.LG2025
Understanding and Mitigating Under-Confidence in GNNs from the Final Layer
Jincheng Huang, Jie Xu, Xiaoshuang Shi +3
Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness on graph-based tasks. However, their predictive confidence is often miscalibrated, typically exhibiting unde…
cs.CV2025
Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation
Jie Xu, Na Zhao, Gang Niu +2
Recently, multi-view learning (MVL) has garnered significant attention due to its ability to fuse discriminative information from multiple views. However, real-world multi-view dat…