Meta-learning For Transfer
Meta-Learning for Transfer is a meta-learning approach that enables agents to acquire generalized learning strategies across simulated environments such that rapid adaptation to real-world dynamics is achieved with minimal retraining.
64
Publications
4.4K
Citations
264
Authors
103
Institutions
Publications per year
2017–2026
63
Parents
64
Meta-Transfer Learning for Few-Shot Learning
Qianru Sun, Yaoyao Liu, Tat‐Seng Chua et al. · 2019 · 1.3K citations
Yu-Xiong Wang, Deva Ramanan, Martial Hebert · Neural Information Processing Systems · 2017 · 267 citations
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Huaxiu Yao, Yiding Liu, Ying Wei et al. · 2019 · 240 citations · Full text
Computing, Data Science, Geospatial Artificial Intelligence +15
1–5 of 64
264
École Polytechnique Fédérale de Lausanne, Switzerland
2 publications · 33 citations
2
h-index
2
h-index
University at Buffalo, State University of New York, United States
2 publications · 60 citations
2
h-index
2
h-index
2
h-index
1–5 of 264
103
Tongji University
Shanghai, China
4 h-index
10 publications
365 citations
National University of Singapore
Singapore, Singapore
4 h-index
8 publications
3K citations
Tencent (China)
Shenzhen, China
3 h-index
4 publications
365 citations
Tsinghua University
Beijing, China
3 h-index
6 publications
850 citations
Stanford University
Stanford, United States
2 h-index
3 publications
51 citations

University of Manchester
Manchester, United Kingdom
2 h-index
2 publications
56 citations
China University of Petroleum, East China
Qingdao, China
2 h-index
11 publications
338 citations

Nanjing University of Information Science and Technology
Nanjing, China
2 h-index
3 publications
148 citations
1–8 of 103
Repository · 13 publications · 365 citations
11
h-index
MSSP · 3 publications · 299 citations
3
h-index
3 publications · 240 citations
3
h-index
2 publications · 67 citations
2
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2 publications · 296 citations
2
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1–5