Post-processing Of Predictions

Post-Processing of Predictions is a computational procedure applied after initial model inference to refine, calibrate, or adjust raw prediction outputs in order to reduce false positives by incorporating domain-specific rules, thresholding mechanisms, or contextual constraints.

18

Publications

1.1K

Citations

78

Authors

43

Institutions

Publications per year

2017–2026

7

Authors

78

  1. SX
    Stella X. Yu

    University of California, Berkeley, United States

    2 publications · 157 citations

    2

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  2. LT
    Lorenzo Torresani

    Dartmouth College, United States

    2 publications · 157 citations

    2

    h-index

  3. GB
    Gedas Bertasius

    University of North Carolina at Chapel Hill, United States

    2 publications · 157 citations

    2

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  4. JS
    Jianbo Shi

    University of Pennsylvania, United States

    2 publications · 157 citations

    2

    h-index

  5. RI
    Rune Inglev

    Technical University of Denmark, Denmark

    1 publications · 44 citations

    1

    h-index

1–5 of 78

Venues

  1. Pattern Recognition

    1 publications · 11 citations

    1

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  2. The Journal of the Acoustical Society of America

    JASA · 1 publications · 48 citations

    1

    h-index

  3. Neural Computing and Applications

    1 publications · 10 citations

    1

    h-index

  4. 1

    h-index

  5. Alternatives to Laboratory Animals

    1 publications · 15 citations

    1

    h-index

1–5