Distribution Learning is a computational framework within the sublinear algorithms domain that aims to infer properties or approximate the structure of an underlying probability distribution from sublinearly many samples or queries.
210
Publications
11.7K
Citations
605
Authors
247
Institutions
Publications per year
2017–2026
137
Parents
Publications
210
Mean-Variance Loss for Deep Age Estimation from a Face
Hongyu Pan, Hu Han, Shiguang Shan et al. · 2018 · 239 citations
Computing, Computer Science, Aging +15
1–5 of 210
Authors
605
- XG
14
h-index
- RA
7
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- ID
7
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- JZ
5
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- PL
5
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1–5 of 605
Institutions
247
Southeast University
Nanjing, China
13 h-index
38 publications
2.5K citations
Nanjing University
Nanjing, China
10 h-index
22 publications
927 citations
Massachusetts Institute of Technology
Cambridge, United States
9 h-index
15 publications
605 citations
Columbia University
New York, United States
8 h-index
11 publications
623 citations
Nanjing University of Science and Technology
Nanjing, China
6 h-index
12 publications
707 citations
Princeton University
Princeton, United States
5 h-index
9 publications
606 citations
Xi'an Jiaotong University
Xi'an, China
5 h-index
22 publications
619 citations
University of the Basque Country
Leioa, Spain
5 h-index
13 publications
491 citations
1–8 of 247
Venues
- arXiv (Cornell University)
Repository · 26 publications · 985 citations
16
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 7 publications · 436 citations
7
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- Neurocomputing
7 publications · 292 citations
7
h-index
- IEEE Transactions on Knowledge and Data Engineering
TKDE · 5 publications · 897 citations
5
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- Information Sciences
4 publications · 132 citations
4
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1–5