Pairwise Loss

Pairwise Loss is a type of loss function in machine learning that quantifies the discrepancy between ordered pairs of predictions by penalizing inversions in relative ranking, thereby optimizing models for tasks involving pairwise comparison or preference learning.

144

Publications

9.5K

Citations

502

Authors

194

Institutions

Publications per year

2017–2026

108

Authors

502

  1. YY
    Yiming Ying

    University at Albany, State University of New York, United States

    4 publications · 90 citations

    4

    h-index

  2. YL
    Yunwen Lei

    City University of Hong Kong, Hong Kong SAR China

    3 publications · 43 citations

    3

    h-index

  3. DZ
    Ding‐Xuan Zhou

    City University of Hong Kong, Hong Kong SAR China

    3 publications · 86 citations

    3

    h-index

  4. XT
    Xiaoguang Tu

    University of Electronic Science and Technology of China, China

    2 publications · 111 citations

    2

    h-index

  5. YW
    Yichen Wei

    Microsoft Research Asia (China), China

    2 publications · 1K citations

    2

    h-index

1–5 of 502

Venues

  1. arXiv (Cornell University)

    Repository · 16 publications · 447 citations

    12

    h-index

  2. 5

    h-index

  3. IEEE Access

    4 publications · 66 citations

    4

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 178 citations

    4

    h-index

  5. IEEE Transactions on Multimedia

    4 publications · 349 citations

    4

    h-index

1–5