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Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 1051–1060 of 1963 papers

TitleStatusHype
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference—0
Disentangling Derivatives, Uncertainty and Error in Gaussian Process ModelsCode1
Mapping Leaf Area Index with a Smartphone and Gaussian Processes—0
Physics-Aware Gaussian Processes in Remote Sensing—0
Deep Gaussian Processes for geophysical parameter retrieval—0
Spectral band selection for vegetation properties retrieval using Gaussian processes regression—0
Nonlinear Distribution Regression for Remote Sensing Applications—0
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
Task-Agnostic Amortized Inference of Gaussian Process HyperparametersCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified