Approximate Machine Learning
Approximate Machine Learning is a machine learning methodology that belongs to the class of computational techniques designed for scalable and efficient learning from large-scale data, differentiated by its deliberate trade-off of precision for computational efficiency through controlled approximations in model training, inference, or data representation.
118
Publications
11.4K
Citations
340
Authors
157
Institutions
Publications per year
2017–2026
57
118
Neural Acceleration for General-Purpose Approximate Programs
Hadi Esmaeilzadeh, Adrian Sampson, Luís Ceze et al. · 2012 · 624 citations
Ian Goodfellow, David Warde-Farley, Mehdi Mirza et al. · 2013 · 607 citations
Ian Goodfellow, David Warde-Farley, Mehdi Mirza et al. · 2013 · 451 citations
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157
University of Washington
Seattle, United States
5 h-index
11 publications
2.3K citations

University of Genoa
Genoa, Italy
4 h-index
15 publications
392 citations
Politecnico di Milano
Milan, Italy
3 h-index
4 publications
231 citations
Microsoft (United States)
Redmond, United States
3 h-index
3 publications
752 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
8 publications
1.2K citations

University of Toronto
Toronto, Canada
3 h-index
6 publications
615 citations
The University of Texas at Austin
Austin, United States
3 h-index
7 publications
89 citations

Washington University in St. Louis
St Louis, United States
3 h-index
10 publications
400 citations
1–8 of 157
Repository · 11 publications · 302 citations
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3 publications · 116 citations
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3 publications · 119 citations
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1 publications · 38 citations
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Conference · 3 publications · 143 citations
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