Benchmarking Machine Learning: How Fast Can Your Algorithms Go?

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dc.contributor.authorNing, Zeyu
dc.contributor.authorIradukunda, Hugues Nelson
dc.contributor.authorZhang, Qingquan
dc.contributor.authorZhu, Ting
dc.date.accessioned2021-06-11T14:01:58Z
dc.date.available2021-06-11T14:01:58Z
dc.date.issued2021-01-08
dc.description.abstractThis paper is focused on evaluating the effect of some different techniques in machine learning speed-up, including vector caches, parallel execution, and so on. The following content will include some review of the previous approaches and our own experimental results.en_US
dc.description.urihttps://arxiv.org/abs/2101.03219en_US
dc.format.extent6 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2nr1h-x5az
dc.identifier.citationNing, Zeyu et al.; Benchmarking Machine Learning: How Fast Can Your Algorithms Go?; Machine Learning, 8 Jan, 2021; https://arxiv.org/abs/2101.03219en_US
dc.identifier.urihttp://hdl.handle.net/11603/21724
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectmethods of speeding up machine learningen_US
dc.subjectvector cachesen_US
dc.subjectparallel executionen_US
dc.titleBenchmarking Machine Learning: How Fast Can Your Algorithms Go?en_US
dc.typeTexten_US

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