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
Parents
18
Convolutional Random Walk Networks for Semantic Image Segmentation
Gedas Bertasius, Lorenzo Torresani, Stella X. Yu et al. · 2017 · 143 citations
The role of the postaudit in model validation
Mary P. Anderson, William Woessner · Advances in Water Resources · 1992 · 126 citations
1–5 of 18
78
2
h-index
2
h-index
University of North Carolina at Chapel Hill, United States
2 publications · 157 citations
2
h-index
2
h-index
1
h-index
1–5 of 78
43

International Computer Science Institute
Berkeley, United States
1 h-index
1 publications
143 citations
Google (United States)
Mountain View, United States
1 h-index
4 publications
104 citations
University of Alabama
Tuscaloosa, United States
1 h-index
4 publications
104 citations
University of California, Berkeley
Berkeley, United States
1 h-index
1 publications
143 citations

Universidad Politécnica de Madrid
Madrid, Spain
1 h-index
2 publications
50 citations
European Federation of Pharmaceutical Industries and Associations
Brussels, Belgium
1 h-index
1 publications
15 citations
European Food Safety Authority
Parma, Italy
1 h-index
1 publications
15 citations

Universitatea Națională de Știință și Tehnologie Politehnica București
Bucharest, Romania
1 h-index
1 publications
48 citations
1–8 of 43
1 publications · 11 citations
1
h-index
JASA · 1 publications · 48 citations
1
h-index
1 publications · 10 citations
1
h-index
1 publications · 27 citations
1
h-index
1 publications · 15 citations
1
h-index
1–5