3 papers
cs.LG2026
Multi-Modal Anomaly Detection: A Survey
Xudong Mou, Zexin Wu, Chuan Luo +4
Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-critical applications such as…
cs.DC2026
CrossPool: Efficient Multi-LLM Serving for Cold MoE Models through KV-Cache and Weight Disaggregation
Zhuoren Ye, Tianyu Wo, Dinghao Xue +4
Emerging LLM services increasingly host many sparse MoE models, yet most models receive sparse requests and remain cold. This creates a GPU memory problem: model weights are stable…
cs.LG2025
An Improved Time Series Anomaly Detection by Applying Structural Similarity
Tiejun Wang, Rui Wang, Xudong Mou +4
Effective anomaly detection in time series is pivotal for modern industrial applications and financial systems. Due to the scarcity of anomaly labels and the high cost of manual la…