Meta-regularization
Meta-Regularization is a meta-learning framework that imposes structural constraints on the learning algorithm’s inductive bias by regularizing the meta-learned prior knowledge transfer across tasks to enhance generalization.
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2.3K
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71
Authors
32
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2017–2026
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Regularized gene selection in cancer microarray meta-analysis
Shuangge Ma, Jian Huang · BMC Bioinformatics · 2009 · 840 citations · Full text
BlockMix: Meta Regularization and Self-Calibrated Inference for Metric-Based Meta-Learning
Hao Tang, Zechao Li, Zhimao Peng et al. · 2020 · 108 citations
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Google (United States)
Mountain View, United States
4 h-index
8 publications
301 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
2 h-index
6 publications
184 citations
University of Michigan
Ann Arbor, United States
2 h-index
2 publications
50 citations
The University of Texas at Austin
Austin, United States
2 h-index
4 publications
230 citations

Southern University of Science and Technology
Shenzhen, China
1 h-index
1 publications
20 citations
Kootenay Association for Science & Technology
Rossland, Canada
1 h-index
2 publications
100 citations
Beijing Research Institute of Mechanical and Electrical Technology
Beijing, China
1 h-index
1 publications
17 citations
Robert Bosch (India)
Bengaluru, India
1 h-index
3 publications
42 citations
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Repository · 3 publications · 122 citations
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Conference · 2 publications · 64 citations
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1 publications · 54 citations
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Conference · 1 publications · 404 citations
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1 publications · 74 citations
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