Optimization-based Meta-learning
Optimization-Based Meta-Learning is a meta-learning approach that employs optimization procedures as the primary mechanism to enable rapid adaptation to new tasks by learning an effective initialization or update rule through experience.
85
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5.2K
Citations
328
Authors
146
Institutions
Publications per year
2017–2026
72
Parents
85
A survey of deep meta-learning
Artificial Intelligence Review · 2021 · 358 citations · Full text
Diverse Few-Shot Text Classification with Multiple Metrics
Mo Yu, Xiaoxiao Guo, Jinfeng Yi et al. · 2018 · 233 citations · Full text
Sajjad Nematzadeh, Farzad Kiani, Mahsa Torkamanian-Afshar · Computational Biology and Chemistry · 2021 · 221 citations
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146

University of Electronic Science and Technology of China
Chengdu, China
4 h-index
14 publications
514 citations
Tsinghua University
Beijing, China
4 h-index
10 publications
332 citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
3 h-index
5 publications
213 citations
Institute for Infocomm Research
Singapore, Singapore
2 h-index
2 publications
70 citations
Nanjing University
Nanjing, China
2 h-index
4 publications
176 citations
University of Science and Technology of China
Hefei, China
2 h-index
5 publications
327 citations

Beijing University of Posts and Telecommunications
Beijing, China
2 h-index
2 publications
48 citations
University of California, Berkeley
Berkeley, United States
2 h-index
2 publications
256 citations
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Repository · 6 publications · 346 citations
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5 publications · 295 citations
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4 publications · 123 citations
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3 publications · 227 citations
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LNCS · Book series · 2 publications · 28 citations
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