Backpropagation

Backpropagation is a supervised learning algorithm for training artificial neural networks that computes the gradient of the loss function with respect to the weights of the network by applying the chain rule of calculus in a backward pass through the network.

5.8K

Publications

608K

Citations

14.8K

Authors

3.4K

Institutions

Publications per year

2017–2026

2K

Variants

Backward PropagationBackward Propagation Of Errors

Authors

14.8K

  1. MA
    Muhammad Asif Zahoor Raja

    COMSATS University Islamabad, Pakistan

    44 publications · 1.9K citations

    26

    h-index

  2. MS
    Muhammad Shoaib

    COMSATS University Islamabad, Pakistan

    25 publications · 1.3K citations

    21

    h-index

  3. AW
    Anjar Wanto

    Universitas Tunas Pembangunan, Indonesia

    26 publications · 1.1K citations

    20

    h-index

  4. ZS
    Zulqurnain Sabir

    Hazara University, Pakistan

    22 publications · 671 citations

    17

    h-index

  5. MM
    M.T. Manry

    The University of Texas at Arlington, United States

    23 publications · 630 citations

    16

    h-index

1–5 of 14.8K

Venues

  1. IEEE Transactions on Neural Networks

    199 publications · 45.2K citations

    90

    h-index

  2. Neural Networks

    98 publications · 46.6K citations

    54

    h-index

  3. arXiv (Cornell University)

    Repository · 205 publications · 29.7K citations

    53

    h-index

  4. Neural Computation

    87 publications · 137.7K citations

    50

    h-index

  5. Neurocomputing

    87 publications · 7.9K citations

    41

    h-index

1–5