Reduction Cuts
Reduction Cuts is a pruning technique in machine learning that belongs to the class of structured sparsity methods, distinguished by its strategy of iteratively removing redundant or less significant network components based on predefined structural criteria to achieve model compression and improved computational efficiency.
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2017–2026
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A MULTI-SURVEY APPROACH TO WHITE DWARF DISCOVERY
The Astronomical Journal · 2012 · 26 citations · Full text
Natural Sciences, Astronomy, Physics +17
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University of Washington
Seattle, United States
2 h-index
5 publications
221 citations
University of Michigan
Ann Arbor, United States
1 h-index
2 publications
20 citations
Université de Montréal
Montreal, Canada
1 h-index
2 publications
52 citations

Florida Institute of Technology
Melbourne, United States
1 h-index
1 publications
117 citations
NOAA National Marine Fisheries Service Southwest Fisheries Science Center
La Jolla, United States
1 h-index
1 publications
117 citations

Harvard University Press
Cambridge, United States
1 h-index
1 publications
28 citations

Western Sydney University
Richmond, Australia
1 h-index
1 publications
117 citations

University of British Columbia
Vancouver, Canada
1 h-index
1 publications
117 citations
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1 publications · 10 citations
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SL · 1 publications · 18 citations
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1 publications · 15 citations
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Repository · 1 publications · 14 citations
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1 publications · 28 citations
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