1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2026★ 1 cited
Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels
Zhu Wang, Junnan Dong, Shuang Zhou +3
Graph anomaly detection (GAD) is widely applied in many areas, such as financial fraud detection and social spammer detection. Anomalous nodes in the graph not only impact their ow…
cs.LG2024
Denoising-Aware Contrastive Learning for Noisy Time Series
Shuang Zhou, Daochen Zha, Xiao Shen +3
Time series self-supervised learning (SSL) aims to exploit unlabeled data for pre-training to mitigate the reliance on labels. Despite the great success in recent years, there is l…