3 papers
cs.LG2025
Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Services
Yue Xing, Yingnan Deng, Heyao Liu +3
This paper addresses the challenges of complex dependencies and diverse anomaly patterns in cloud service environments by proposing a dependency modeling and anomaly detection meth…
cs.CL2025
Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models
Ming Gong, Yingnan Deng, Nia Qi +3
This paper addresses the issues of parameter redundancy, rigid structure, and limited task adaptability in the fine-tuning of large language models. It proposes an adapter-based fi…
cs.LG2025
Graph Neural Network and Transformer Integration for Unsupervised System Anomaly Discovery
Yun Zi, Ming Gong, Zhihao Xue +3
This study proposes an unsupervised anomaly detection method for distributed backend service systems, addressing practical challenges such as complex structural dependencies, diver…