Neural Architecture Search
Neural Architecture Search is a meta-learning framework that automates the design of neural network architectures by searching over a predefined space of architectural components to identify optimal configurations according to a performance objective.
1.2K
Publications
94.8K
Citations
3.8K
Authors
835
Institutions
Publications per year
2017–2026
1.2K
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Neural Architecture Search with Reinforcement Learning
Barret Zoph, Quoc V. Le · arXiv (Cornell University) · 2016 · 3.9K citations · Full text
Regularized Evolution for Image Classifier Architecture Search
Esteban Real, Alok Aggarwal, Yanping Huang et al. · 2019 · 2.7K citations
Neural Architecture Search: A Survey
Thomas Elsken, Jan Hendrik Metzen, Frank Hutter · arXiv (Cornell University) · 2018 · 1.4K citations · Full text
DARTS: Differentiable Architecture Search
Hanxiao Liu, Karen Simonyan, Yiming Yang · arXiv (Cornell University) · 2018 · 1.4K citations · Full text
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Google (United States)
Mountain View, United States
31 h-index
197 publications
50.1K citations
University of Chinese Academy of Sciences
Beijing, China
26 h-index
91 publications
4.4K citations
Tsinghua University
Beijing, China
24 h-index
104 publications
4K citations

Chinese Academy of Sciences
Beijing, China
23 h-index
102 publications
4.7K citations
University of Technology Sydney
Sydney, Australia
21 h-index
44 publications
5.7K citations

Peking University
Beijing, China
20 h-index
85 publications
4.7K citations

Sichuan University
Chengdu, China
20 h-index
75 publications
3.2K citations

The University of Sydney
Sydney, Australia
19 h-index
60 publications
3.3K citations
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Repository · 345 publications · 35K citations
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Conference · 29 publications · 2.3K citations
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28 publications · 1.6K citations
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22 publications · 2.2K citations
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21 publications · 1.3K citations
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