Logistic Loss
Logistic Loss is a loss function in machine learning that quantifies the discrepancy between predicted probabilities and binary true labels by applying the logarithmic penalty derived from the logistic (sigmoid) transformation.
121
Publications
8.2K
Citations
328
Authors
162
Institutions
Publications per year
2017–2026
65
Parents
121
Energy-based Generative Adversarial Network
Junbo Zhao, Michaël Mathieu, Yann LeCun · arXiv (Cornell University) · 2016 · 895 citations · Full text
A Comprehensive Survey of Loss Functions in Machine Learning
Qi Wang, Yue Ma, Kun Zhao et al. · Annals of Data Science · 2020 · 693 citations
Are Loss Functions All the Same?
Lorenzo Rosasco, Ernesto De Vito, Andrea Caponnetto et al. · Neural Computation · 2004 · 505 citations · Full text
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Institut national de recherche en sciences et technologies du numérique, France
5 publications · 246 citations
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California Institute of Technology, United States
4 publications · 128 citations
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162
University of Illinois Urbana-Champaign
Urbana, United States
6 h-index
10 publications
2.2K citations
University of California, Santa Cruz
Santa Cruz, United States
5 h-index
8 publications
249 citations
Tsinghua University
Beijing, China
5 h-index
14 publications
430 citations
Toyota Technological Institute at Chicago
Chicago, United States
4 h-index
6 publications
222 citations
Nanjing University
Nanjing, China
4 h-index
6 publications
184 citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
12 publications
381 citations
University of Southern California
Los Angeles, United States
3 h-index
3 publications
52 citations
Technion – Israel Institute of Technology
Haifa, Israel
3 h-index
6 publications
234 citations
1–8 of 162
Repository · 31 publications · 2.2K citations
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Conference · 6 publications · 163 citations
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Conference · 3 publications · 50 citations
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3 publications · 181 citations
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Conference · 3 publications · 347 citations
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