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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 761–770 of 1963 papers

TitleStatusHype
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation—0
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems—0
Gaussian Processes with State-Dependent Noise for Stochastic Control—0
Simultaneous Reconstruction and Uncertainty Quantification for Tomography—0
Gaussian Process for Trajectories—0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—0
Attentive Gaussian processes for probabilistic time-series generation—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
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Benchmark Results

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