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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 901–910 of 1963 papers

TitleStatusHype
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
Gaussian Process Optimization with Mutual Information—0
Gaussian Process on the Product of Directional Manifolds—0
Gaussian Process Neurons Learn Stochastic Activation Functions—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Gaussian Process Neurons—0
Gaussian Process Morphable Models—0
Gaussian Process Molecule Property Prediction with FlowMO—0
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Benchmark Results

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