Cross-entropy Loss

Cross-Entropy Loss is a loss function that measures the dissimilarity between predicted probability distributions and true class labels in classification tasks by quantifying the divergence using the negative log-likelihood of the correct class under the softmax or sigmoid output.

1.3K

Publications

136.4K

Citations

4.9K

Authors

1.3K

Institutions

Publications per year

2017–2026

1.2K

Authors

4.9K

  1. IB
    Ismail Ben Ayed

    École de Technologie Supérieure, Canada

    8 publications · 282 citations

    8

    h-index

  2. RY
    Raymond Y.K. Lau

    City University of Hong Kong, Hong Kong SAR China

    6 publications · 5.8K citations

    6

    h-index

  3. XW
    Xiaogang Wang

    European Organization for Nuclear Research, Switzerland

    6 publications · 1.3K citations

    6

    h-index

  4. QL
    Qing Li

    City University of Hong Kong, Hong Kong SAR China

    6 publications · 5.8K citations

    6

    h-index

  5. DY
    Dong Yu

    Microsoft (United States), United States

    6 publications · 547 citations

    6

    h-index

1–5 of 4.9K

Venues

  1. arXiv (Cornell University)

    Repository · 209 publications · 15.2K citations

    55

    h-index

  2. IEEE Access

    71 publications · 4.7K citations

    37

    h-index

  3. Remote Sensing

    31 publications · 1.1K citations

    23

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 30 publications · 2.8K citations

    23

    h-index

  5. 17

    h-index

1–5