Web: http://arxiv.org/abs/2201.12465

June 24, 2022, 1:11 a.m. | Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Ste

cs.LG updates on arXiv.org arxiv.org

As the computational requirements for machine learning systems and the size
and complexity of machine learning frameworks increases, essential framework
innovation has become challenging. While computational needs have driven recent
compiler, networking, and hardware advancements, utilization of those
advancements by machine learning tools is occurring at a slower pace. This is
in part due to the difficulties involved in prototyping new computational
paradigms with existing frameworks. Large frameworks prioritize machine
learning researchers and practitioners as end users and pay comparatively …

arxiv enabling innovation learning lg machine machine learning tools

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