4 citations · 10 across the 11 of their papers we have counts for
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cs.LG2025
DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
Qingfeng Chen, Haojin Zeng, Jingyi Jie +2
With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGAD seeks to identify entire graph…
cs.AI2025
Harnessing LLM for Noise-Robust Cognitive Diagnosis in Web-Based Intelligent Education Systems
Guixian Zhang, Guan Yuan, Ziqi Xu +4
Cognitive diagnostics in the Web-based Intelligent Education System (WIES) aims to assess students' mastery of knowledge concepts from heterogeneous, noisy interactions. Recent wor…
cs.CV2025★ 1 cited
Efficient Adaptive Label Refinement for Label Noise Learning
Wenzhen Zhang, Debo Cheng, Guangquan Lu +3
Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing manually defined crit…