Transformer Models

Transformer Models is a neural network architecture distinguished by its use of self-attention mechanisms to process sequential data in parallel, enabling highly effective modeling of long-range dependencies in time series prediction and other sequence-based tasks.

5.6K

Publications

493.8K

Citations

17.1K

Authors

2.8K

Institutions

Publications per year

2017–2026

4.9K

Authors

17.1K

  1. SW
    Shinji Watanabe

    Johns Hopkins University, United States

    20 publications · 2K citations

    17

    h-index

  2. FW
    Furu Wei

    Microsoft Research Asia (China), China

    21 publications · 3.6K citations

    16

    h-index

  3. TQ
    Tao Qin

    Microsoft Research Asia (China), China

    16 publications · 1.8K citations

    15

    h-index

  4. YT
    Yi Tay

    Nanyang Technological University, Singapore

    14 publications · 1.1K citations

    14

    h-index

  5. SL
    Shujie Liu

    Microsoft Research Asia (China), China

    14 publications · 3.1K citations

    14

    h-index

1–5 of 17.1K

Venues

  1. arXiv (Cornell University)

    Repository · 819 publications · 91K citations

    114

    h-index

  2. IEEE Transactions on Power Delivery

    147 publications · 13.1K citations

    58

    h-index

  3. 54

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 95 publications · 18.3K citations

    46

    h-index

  5. IEEE Access

    98 publications · 5K citations

    41

    h-index

1–5