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Found 7 papers, 3 papers with code
Normalize Then Propagate: Efficient Homophilous Regularization for Few-shot Semi-Supervised Node Classification
In this paper, we first analyze the restrictions of GNNs generalization from the perspective of supervision signals in the context of few-shot semi-supervised node classification.
Enhancing Robustness of CLIP to Common Corruptions through Bimodal Test-Time Adaptation
Although open-vocabulary classification models like Contrastive Language Image Pretraining (CLIP) have demonstrated strong zero-shot learning capabilities, their robustness to common image corruptions remains poorly understood.
GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck
The key idea behind GaGSL is to learn a compact and informative graph structure for node classification tasks.
Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
Next, we propose a clean labels oriented link that connects unlabeled nodes to cleanly labeled nodes, aimed at mitigating label sparsity and promoting supervision propagation.
Similarity-Navigated Conformal Prediction for Graph Neural Networks
In this paper, we empirically show that for each node, aggregating the non-conformity scores of nodes with the same label can improve the efficiency of conformal prediction sets while maintaining valid marginal coverage.
PALM: Pushing Adaptive Learning Rate Mechanisms for Continual Test-Time Adaptation
Real-world vision models in dynamic environments face rapid shifts in domain distributions, leading to decreased recognition performance.
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels
We introduce LaplaceConfidence, a method that to obtain label confidence (i. e., clean probabilities) utilizing the Laplacian energy.
