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 1921–1930 of 1963 papers

TitleStatusHype
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs—0
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes—0
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes—0
Inter-domain Deep Gaussian Processes—0
Inter-domain Deep Gaussian Processes with RKHS Fourier Features—0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
Interpolation pour l'augmentation de donnees : Application à la gestion des adventices de la canne a sucre a la Reunion—0
Interpretable deep Gaussian processes with moments—0
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage—0
Interrelation of equivariant Gaussian processes and convolutional neural networks—0
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Benchmark Results

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