Online Meta-learning
Online Meta-Learning is a meta-learning framework that dynamically updates the meta-learner in a sequential, incremental manner as tasks arrive over time, distinguishing itself from batch meta-learning approaches by its continuous adaptation to new tasks without reprocessing past data.
95
Publications
4.1K
Citations
332
Authors
133
Institutions
Publications per year
2017–2026
80
Parents
95
High-Performance Long-Term Tracking With Meta-Updater
Kenan Dai, Yunhua Zhang, Dong Wang et al. · 2020 · 283 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
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133
Carnegie Mellon University
Pittsburgh, United States
4 h-index
8 publications
284 citations
Nanyang Technological University
Singapore, Singapore
4 h-index
8 publications
431 citations
Google DeepMind (United Kingdom)
London, United Kingdom
4 h-index
12 publications
886 citations

University of Alberta
Edmonton, Canada
3 h-index
6 publications
83 citations
Google (United States)
Mountain View, United States
3 h-index
3 publications
183 citations
Stanford University
Stanford, United States
3 h-index
7 publications
185 citations
University of California, Berkeley
Berkeley, United States
3 h-index
13 publications
1.2K citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
2 h-index
3 publications
114 citations
1–8 of 133
Repository · 21 publications · 1K citations
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2 publications · 174 citations
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2 publications · 67 citations
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Conference · 2 publications · 45 citations
2
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1 publications · 69 citations
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