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 711–720 of 1963 papers

TitleStatusHype
Gait learning for soft microrobots controlled by light fields—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Gaussian Processes indexed on the symmetric group: prediction and learning—0
A Perspective on Gaussian Processes for Earth Observation—0
Bayesian Variational Optimization for Combinatorial Spaces—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
A computationally lightweight safe learning algorithm—0
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Benchmark Results

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