June 7, 2024, 4:43 a.m. | Can Yaras, Peng Wang, Laura Balzano, Qing Qu

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04112v1 Announce Type: new
Abstract: While overparameterization in machine learning models offers great benefits in terms of optimization and generalization, it also leads to increased computational requirements as model sizes grow. In this work, we show that by leveraging the inherent low-dimensional structures of data and compressible dynamics within the model parameters, we can reap the benefits of overparameterization without the computational burdens. In practice, we demonstrate the effectiveness of this approach for deep low-rank matrix completion as well as …

abstract arxiv benefits computational cs.ai cs.lg data dynamics eess.sp leads low machine machine learning machine learning models optimization requirements show stat.ml terms type while work

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