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 1651–1660 of 1963 papers

TitleStatusHype
Deep Gaussian Processes with Decoupled Inducing Inputs—0
Multiscale Sparse Microcanonical Models—0
PHOENICS: A universal deep Bayesian optimizerCode0
Intrinsic Gaussian processes on complex constrained domains—0
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks—0
Gaussian Process Neurons—0
Sparse Covariance Modeling in High Dimensions with Gaussian Processes—0
Distributed non-parametric deep and wide networks—0
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data—0
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distributionCode0
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Benchmark Results

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