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Found 4 papers, 1 papers with code
Simultaneous Missing Value Imputation and Structure Learning with Groups
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve.
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion
Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations.
Activation-level uncertainty in deep neural networks
Current approaches for uncertainty estimation in deep learning often produce too confident results.
Remote Sensing Image Classification with Large Scale Gaussian Processes
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources.
