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Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning models which leverage the flexibility of deep learning to produce well-calibrated predictions and naturally handle off-the-grid and missing data.
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Manifold gaussian processes for regression, 2016
Roberto Calandra, Jan Peters, Carl Edward Rasmussen, and Marc Peter Deisenroth · 2016
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Intercomparison of machine learning methods for statistical downscaling: The case of daily and extreme precipitation
Thomas Vandal, Evan Kodra, and Auroop R Ganguly · 2019
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Downscaling numerical weather models with gans
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Deepsd: Generating high resolution climate change projections through single image super-resolution
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Downscaling future climate change projections over puerto rico using a non-hydrostatic atmospheric model
Amit Bhardwaj, Vasubandhu Misra, Akhilesh Mishra, Adrienne Wootten, Ryan Boyles, JH Bowden, and Adam J Terando · 2018
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Empirical evaluation of neural process objectives
Tuan Anh Le, Hyunjik Kim, Marta Garnelo, Dan Rosenbaum, Jonathan Schwarz, and Yee Whye Teh · 2018
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Statistical downscaling and bias correction for climate research
Douglas Maraun and Martin Widmann · 2018
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Statistical downscaling of precipitation using long short-term memory recurrent neural networks
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Scalable exact inference in multi-output gaussian processes
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Meta-learning stationary stochastic process prediction with convolutional neural processes, 2020
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Convolutional conditional neural processes, 2020
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Normalizing flows: An introduction and review of current methods
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Climate downscaling using ynet: A deep convolutional network with skip connections and fusion
Yumin Liu, Auroop R Ganguly, and Jennifer Dy · 2020
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Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, and Richard E. Turner · 2021
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Equivariant learning of stochastic fields: Gaussian processes and steerable conditional neural processes, 2021
Peter Holderrieth, Michael Hutchinson, and Yee Whye Teh · 2021
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Group equivariant conditional neural processes
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Gp-convcnp: Better generalization for convolutional conditional neural processes on time series data
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Convolutional conditional neural processes for local climate downscaling
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