Attention Mechanisms

Attention Mechanism is a computational model in deep learning that selectively enhances or suppresses elements of input representations by dynamically assigning varying weights to different parts of the data, based on their relevance to the current processing task.

2.9K

Publications

328.8K

Citations

10.5K

Authors

2.1K

Institutions

Publications per year

2017–2026

2.4K

Authors

10.5K

  1. BA
    Brian A. Anderson

    Texas A&M University, United States

    8 publications · 917 citations

    8

    h-index

  2. HH
    Haifeng Hu

    Sun Yat-sen University, China

    8 publications · 253 citations

    8

    h-index

  3. QH
    Qibin Hou

    Nankai University, China

    8 publications · 7.8K citations

    8

    h-index

  4. UA
    Ulrich Ansorge

    University of Vienna, Austria

    7 publications · 192 citations

    7

    h-index

  5. YT
    Yi Tay

    Nanyang Technological University, Singapore

    7 publications · 220 citations

    7

    h-index

1–5 of 10.5K

Venues

  1. arXiv (Cornell University)

    Repository · 349 publications · 36.1K citations

    71

    h-index

  2. IEEE Access

    128 publications · 4.9K citations

    42

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 56 publications · 4.9K citations

    37

    h-index

  4. Remote Sensing

    59 publications · 3K citations

    30

    h-index

  5. 28

    h-index

1–5