Distributed Machine Learning
Distributed Machine Learning is a subfield of Scalable Machine Learning that leverages distributed computing systems to parallelize and coordinate the training and inference processes of machine learning models across multiple interconnected nodes or devices, thereby enabling efficient processing of large-scale datasets and models.
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171.1K
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1.5K
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2017–2026
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martı́n Abadi, Ashish Agarwal, Paul Barham et al. · arXiv (Cornell University) · 2016 · 9.8K citations · Full text
Large Scale Distributed Deep Networks
Jay B. Dean, Greg S. Corrado, Rajat Monga et al. · 2012 · 2.9K citations
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Carnegie Mellon University
Pittsburgh, United States
35 h-index
172 publications
26.7K citations
University of California, Berkeley
Berkeley, United States
26 h-index
132 publications
16.6K citations

Zhejiang University
Hangzhou, China
24 h-index
57 publications
2.8K citations
Tsinghua University
Beijing, China
23 h-index
113 publications
8K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
23 h-index
58 publications
3K citations
ETH Zurich
Zurich, Switzerland
22 h-index
62 publications
4.4K citations

University of Hong Kong
Pok Fu Lam, Hong Kong
22 h-index
39 publications
3.1K citations
Google (United States)
Mountain View, United States
22 h-index
101 publications
48.4K citations
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Repository · 354 publications · 37.7K citations
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40 publications · 3.9K citations
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32 publications · 3.3K citations
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37 publications · 1.5K citations
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TPDS · 28 publications · 2.4K citations
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