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

Authors

3.8K

  1. FH
    Frank Hutter

    University of Freiburg, Germany

    30 publications · 4K citations

    20

    h-index

  2. QV
    Quoc V. Le

    Google (United States), United States

    24 publications · 17.9K citations

    20

    h-index

  3. YS
    Yanan Sun

    Sichuan University, China

    23 publications · 2K citations

    19

    h-index

  4. SH
    Song Han

    Massachusetts Institute of Technology, United States

    19 publications · 3.6K citations

    18

    h-index

  5. BX
    Bing Xue

    Victoria University of Wellington, New Zealand

    21 publications · 2.1K citations

    18

    h-index

1–5 of 3.8K

Venues

  1. arXiv (Cornell University)

    Repository · 345 publications · 35K citations

    72

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 29 publications · 2.3K citations

    22

    h-index

  3. IEEE Access

    28 publications · 1.6K citations

    21

    h-index

  4. IEEE Transactions on Evolutionary Computation

    22 publications · 2.2K citations

    21

    h-index

  5. 20

    h-index

1–5