Parallel Deep Learning is a computational framework within distributed machine learning that leverages data parallelism to simultaneously train deep neural networks across multiple devices or nodes by partitioning and processing mini-batches of data in parallel while sharing model parameters.
650
Publications
59.4K
Citations
2.6K
Authors
588
Institutions
Publications per year
2017–2026
532
Parents
Publications
650
Building high-level features using large scale unsupervised learning
Quoc V. Le · 2013 · 1.7K citations
Parallel convolutional processing using an integrated photonic tensor core
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Authors
2.6K
- YY
11
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- BV
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- AA
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- BKBrian Kingsbury
IBM Research - Thomas J. Watson Research Center, United States
7 publications · 389 citations
7
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- YH
7
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Institutions
588
Chinese Academy of Sciences
Beijing, China
14 h-index
50 publications
5.5K citations
Google (United States)
Mountain View, United States
14 h-index
52 publications
8.8K citations
University of Science and Technology of China
Hefei, China
13 h-index
46 publications
2.3K citations
Stanford University
Stanford, United States
11 h-index
25 publications
7K citations
Microsoft (United States)
Redmond, United States
11 h-index
37 publications
5.8K citations
Tsinghua University
Beijing, China
11 h-index
30 publications
1.5K citations
National University of Defense Technology
Changsha, China
11 h-index
56 publications
2.7K citations
University of Chinese Academy of Sciences
Beijing, China
10 h-index
27 publications
5.2K citations
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Venues
- arXiv (Cornell University)
Repository · 130 publications · 12.4K citations
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- IEEE Access
15 publications · 1.1K citations
14
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- IEEE Transactions on Parallel and Distributed Systems
TPDS · 12 publications · 602 citations
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- Journal of Parallel and Distributed Computing
6 publications · 180 citations
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- Engineering Applications of Artificial Intelligence
5 publications · 245 citations
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