June 7, 2024, 4:43 a.m. | Omar Alhussein, Ning Zhang, Sami Muhaidat, Weihua Zhuang

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03630v1 Announce Type: cross
Abstract: This paper explores the integration of active machine learning (ML) for 6G networks, an area that remains under-explored yet holds potential. Unlike passive ML systems, active ML can be made to interact with the network environment. It actively selects informative and representative data points for training, thereby reducing the volume of data needed while accelerating the learning process. While active learning research mainly focuses on data annotation, we call for a network-centric active learning framework …

abstract acquisition annotation arxiv cs.ai cs.lg cs.ni data data generation environment integration machine machine learning network networks paper potential systems type

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