AI RESEARCH
EMA: Efficient Model Adaptation for Learning-based Systems
arXiv CS.LG
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ArXi:2605.13942v1 Announce Type: new Machine learning (ML) is increasingly applied to optimize system performance in tasks such as resource management and network simulation. Unlike traditional ML tasks (e.g., image classification), networked systems often operate in heterogeneous, long-running, and dynamic environment states, where input conditions (e.g., network loads) and operational objectives can shift over time and across settings. Existing learning-based systems offer little for adaptation, resulting in costly model.