SOTAVerified

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 1421–1430 of 1963 papers

TitleStatusHype
Asymmetric kernel in Gaussian Processes for learning target variance—0
Asynchronous Distributed Variational Gaussian Processes for Regression—0
A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression—0
A temporal model of text periodicities using Gaussian Processes—0
A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation—0
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots—0
Attentive Gaussian processes for probabilistic time-series generation—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
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Benchmark Results

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