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cs.LG2026
Too Sure to Be Safe: Model Calibration for Reliable Log Anomaly Detection
Bin Li, Dongdong Wang, Siyang Lu
Online log anomaly detection is critical for maintaining the reliability of large-scale computing systems. Although recent language model-based log anomaly detectors achieve strong…
cs.LG2024★ 2 cited
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
Fan Xu, Nan Wang, Hao Wu +7
Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to…