Continual Meta-learning
Continual Meta-Learning is a meta-learning paradigm that enables a model to progressively adapt and accumulate knowledge across sequentially encountered tasks while retaining the ability to generalize to new tasks without forgetting previously acquired meta-knowledge.
42
Publications
1.9K
Citations
164
Authors
69
Institutions
Publications per year
2017–2026
38
Parents
42
Tsendsuren Munkhdalai, Hong Yu · arXiv (Cornell University) · 2017 · 214 citations · Full text
Tsendsuren Munkhdalai, Hong Yu · International Conference on Machine Learning · 2017 · 197 citations
A proposition on memes and meta-memes in computing for higher-order learning
Ryan Meuth, Meng‐Hiot Lim, Yew-Soon Ong et al. · Memetic Computing · 2009 · 124 citations
Hierarchically Structured Meta-learning
Huaxiu Yao, Ying Wei, Junzhou Huang et al. · arXiv (Cornell University) · 2019 · 117 citations · Full text
1–5 of 42
164
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International Institute of Information Technology, Hyderabad, India
2 publications · 53 citations
2
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2
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2
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1–5 of 164
69
University of Massachusetts Boston
Boston, United States
3 h-index
6 publications
872 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
5 publications
222 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
5 publications
85 citations
Shandong Institute of Automation
Jinan, China
2 h-index
4 publications
66 citations

Xi'an Jiaotong University
Xi'an, China
2 h-index
2 publications
175 citations

Pennsylvania State University
State College, United States
2 h-index
2 publications
131 citations
McGovern Institute for Brain Research
Cambridge, United States
2 h-index
2 publications
128 citations

The University of Queensland
Brisbane, Australia
2 h-index
5 publications
117 citations
1–8 of 69
Repository · 14 publications · 763 citations
13
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1 publications · 16 citations
1
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Conference · 1 publications · 26 citations
1
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1 publications · 55 citations
1
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Repository · 1 publications · 25 citations
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1–5