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 1401–1410 of 1963 papers

TitleStatusHype
Modulating Surrogates for Bayesian Optimization—0
Modeling Severe Traffic Accidents With Spatial And Temporal Features—0
A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN—0
Sequential Neural ProcessesCode0
Compositionally-Warped Gaussian Processes—0
Multi-task Learning for Aggregated Data using Gaussian ProcessesCode0
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processesCode0
Black-Box Inference for Non-Linear Latent Force Models—0
Multi-resolution Multi-task Gaussian ProcessesCode0
Bayesian Learning from Sequential Data using Gaussian Processes with Signature CovariancesCode0
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Benchmark Results

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