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 1151–1160 of 1963 papers

TitleStatusHype
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
Gait learning for soft microrobots controlled by light fields—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Gaussian behaviors: representations and data-driven control—0
Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety—0
Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations—0
Gaussian Experts Selection using Graphical Models—0
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes—0
Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation—0
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Benchmark Results

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