June 10, 2024, 4:44 a.m. | Nikhil Vyas, Depen Morwani, Rosie Zhao, Gal Kaplun, Sham Kakade, Boaz Barak

stat.ML updates on arXiv.org arxiv.org

arXiv:2306.08590v2 Announce Type: replace-cross
Abstract: The success of SGD in deep learning has been ascribed by prior works to the implicit bias induced by finite batch sizes ("SGD noise"). While prior works focused on offline learning (i.e., multiple-epoch training), we study the impact of SGD noise on online (i.e., single epoch) learning. Through an extensive empirical analysis of image and language data, we demonstrate that small batch sizes do not confer any implicit bias advantages in online learning. In contrast …

abstract arxiv beyond bias cs.lg deep learning impact multiple noise offline online learning prior replace stat.ml study success training type while

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