Scalable Model Evaluation
Scalable Model Evaluation is a systematic methodology within machine learning for big data that enables the efficient and effective assessment of model performance across increasingly large datasets and complex models by leveraging computational scalability and statistical rigor to maintain accuracy and feasibility.
94
Publications
5.9K
Citations
651
Authors
181
Institutions
Publications per year
2017–2026
66
94
Training Compute-Optimal Large Language Models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch et al. · arXiv (Cornell University) · 2022 · 664 citations · Full text
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
Guocheng Qian, Yuchen Li, Houwen Peng et al. · arXiv (Cornell University) · 2022 · 415 citations · Full text
Finding your friends and following them to where you are
Adam Sadilek, Henry Kautz, Jeffrey P. Bigham · 2012 · 349 citations
NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations
Saurabh Garg, Rajkumar Buyya · 2011 · 282 citations · Full text
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181
Google (United States)
Mountain View, United States
5 h-index
13 publications
498 citations
Stanford University
Stanford, United States
3 h-index
29 publications
3K citations
University of Utah
Salt Lake City, United States
3 h-index
4 publications
162 citations
University of Michigan
Ann Arbor, United States
3 h-index
13 publications
755 citations

The University of Melbourne
Melbourne, Australia
3 h-index
6 publications
734 citations
Politecnico di Milano
Milan, Italy
3 h-index
13 publications
391 citations
Technical University of Munich
Munich, Germany
2 h-index
6 publications
198 citations
NOAA Global Systems Laboratory
Boulder, United States
2 h-index
2 publications
102 citations
1–8 of 181
Repository · 21 publications · 1.8K citations
18
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2 publications · 68 citations
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2 publications · 76 citations
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Repository · 1 publications · 12 citations
1
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1 publications · 36 citations
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