4 papers · 1 filter
Long-Tailed Out-of-Distribution Detection with Refined Separate Class Learning
Shuai Feng, Yuxin Ge, Yuntao Du +3
Out-of-distribution (OOD) detection is crucial for deploying robust machine learning models. However, when training data follows a long-tailed distribution, the model's ability to…
GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck
Shuangjie Li, Jiangqing Song, Baoming Zhang +3
Graph neural networks (GNNs) are prominent for their effectiveness in processing graph data for semi-supervised node classification tasks. Most works of GNNs assume that the observ…
Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
Shuangjie Li, Baoming Zhang, Jianqing Song +3
Graph Neural Networks (GNNs) have gained considerable prominence in semi-supervised learning tasks in processing graph-structured data, primarily owing to their message-passing mec…
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels
Mingcai Chen, Yuntao Du, Wei Tang +4
In real-world applications, perfect labels are rarely available, making it challenging to develop robust machine learning algorithms that can handle noisy labels. Recent methods ha…