242 citations · 388 across the 15 of their papers we have counts for
12 papers · 1 filter
Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation
Yongliang Ding, Tao Zhou, Chuang Zhang +3
In real-world scenarios, many large-scale datasets often contain inaccurate labels, i.e., noisy labels, which may confuse model training and lead to performance degradation. To ove…
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Ming Jin, Yizhen Zheng, Yuan-Fang Li +3
Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To…
Learning with Group Noise
Qizhou Wang, Jiangchao Yao, Chen Gong +4
Machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pair…
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Yixin Liu, Zhao Li, Shirui Pan +3
Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recen…
Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
Sheng Wan, Shirui Pan, Jian Yang +1
Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. As one of the most popular…
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Xuxi Chen, Wuyang Chen, Tianlong Chen +4
Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i.e., learning binary classifiers from a large amount of unlabeled data and a few labeled…