Few-shot learning for spatial regression via neural embedding-based Gaussian processes

DOI10.1007/s10994-021-06118-zOpenAlexW3212339352MaRDI QIDQ2163183

Yusuke Tanaka, Tomoharu Iwata

Publication date: 10 August 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-021-06118-z

zbMATH Keywords

neural networksGaussian processesspatial regressionmeta-learningfew-shot learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• PyTorch
• Adam

Cites Work

• Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach
• Estimation in semiparametric spatial regression
• Gaussian Predictive Process Models for Large Spatial Data Sets
• The origins of kriging
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