Cross Entropy
Cross Entropy is a measure of the difference between two probability distributions for a given random variable or set of events, defined as the expected number of bits needed to identify an event drawn from one distribution when using a coding scheme optimized for another distribution.
1.7K
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154.7K
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5.6K
Authors
1.4K
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Publications per year
2017–2026
1.1K
Variants
Cross-entropy
Parents
1.7K
Focal Loss for Dense Object Detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick et al. · 2017 · 25.7K citations
Focal Loss for Dense Object Detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2018 · 9.6K citations
Least Squares Generative Adversarial Networks
Xudong Mao, Qing Li, Haoran Xie et al. · 2017 · 5.2K citations
A Tutorial on the Cross-Entropy Method
Pieter-Tjerk de Boer, Dirk P. Kroese, Shie Mannor et al. · Annals of Operations Research · 2005 · 3K citations · Full text
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Tsinghua University
Beijing, China
24 h-index
100 publications
5.4K citations

Chinese Academy of Sciences
Beijing, China
24 h-index
113 publications
10.8K citations
Technion – Israel Institute of Technology
Haifa, Israel
21 h-index
44 publications
5.8K citations

The University of Queensland
Brisbane, Australia
19 h-index
33 publications
4.9K citations

Shanghai Jiao Tong University
Shanghai, China
19 h-index
65 publications
8.3K citations

Northwestern Polytechnical University
Xi'an, China
17 h-index
53 publications
13.6K citations

Zhejiang University
Hangzhou, China
17 h-index
54 publications
5.4K citations
University of Chinese Academy of Sciences
Beijing, China
17 h-index
69 publications
4.8K citations
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Repository · 200 publications · 14.2K citations
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63 publications · 3.4K citations
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41 publications · 1.3K citations
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23 publications · 894 citations
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Conference · 27 publications · 2.9K citations
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