Entropy-based Representation
Entropy-Based Representation is a formal method for quantifying uncertainty in probabilistic systems by modeling the degree of randomness or unpredictability through information-theoretic entropy measures.
404
Publications
17.9K
Citations
1.1K
Authors
568
Institutions
Publications per year
2017–2026
159
Parents
404
Entropy-based active learning for object recognition
Alex Holub, Pietro Perona, Michael C. Burl · 2008 · 250 citations
Measures of Information and Their Applications.
J. N. Kapur · Biometrics · 1996 · 234 citations
Engineering, Ecology, Mathematics +14
1–5 of 404
1.1K
University of Electronic Science and Technology of China, China
7 publications · 439 citations
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Southwest University
Chongqing, China
8 h-index
14 publications
785 citations

Chinese Academy of Sciences
Beijing, China
7 h-index
16 publications
921 citations

University of Electronic Science and Technology of China
Chengdu, China
6 h-index
11 publications
797 citations

Sapienza University of Rome
Rome, Italy
5 h-index
12 publications
369 citations
AGH University of Krakow
Krakow, Poland
5 h-index
11 publications
954 citations

Xi'an Jiaotong University
Xi'an, China
5 h-index
9 publications
307 citations

Iona College
New Rochelle, United States
5 h-index
5 publications
417 citations
Texas A&M University
College Station, United States
5 h-index
8 publications
725 citations
1–8 of 568
22 publications · 986 citations
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TFS · 8 publications · 1K citations
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8 publications · 316 citations
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6 publications · 605 citations
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Repository · 6 publications · 241 citations
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