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 1211–1220 of 1963 papers

TitleStatusHype
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Skew Gaussian Processes for ClassificationCode1
Longitudinal Deep Kernel Gaussian Process Regression—0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
Global Optimization of Gaussian processes—0
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes—0
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processesCode1
Stable spline identification of linear systems under missing data—0
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Benchmark Results

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