Few-shot learning for spatial regression via neural embedding-based Gaussian processes
Authors:
- 12 November 2021
- journal article
- research article
- Published by Springer Nature in Machine Learning
- Vol. 111 (4) , 1239-1257
Abstract
No abstract availableKeywords
This publication has 24 references indexed in Scilit:
- Projecting boreal bird responses to climate change: the signal exceeds the noiseEcological Applications, 2015
- Discovering regions of different functions in a city using human mobility and POIs2012
- A spatial regression analysis of the influence of topography on monthly rainfall in East AfricaInternational Journal of Climatology, 2011
- Gaussian Predictive Process Models for Large Spatial Data SetsJournal of the Royal Statistical Society Series B: Statistical Methodology, 2008
- Social and political forces as determinants of poverty: A spatial analysisThe Journal of Socio-Economics, 2007
- An empirical evaluation of spatial regression modelsComputers & Geosciences, 2006
- Estimation in semiparametric spatial regressionThe Annals of Statistics, 2006
- Universal Kriging and Cokriging as a Regression Procedure1991
- The origins of krigingMathematical Geology, 1990
- Matrix formulation of co-krigingMathematical Geology, 1982