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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–710 of 1963 papers

TitleStatusHype
Functional Causal Bayesian Optimization—0
Functional Gaussian processes for regression with linear PDE models—0
Gaussian Processes for Analyzing Positioned Trajectories in Sports—0
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models—0
A Perspective on Gaussian Processes for Earth Observation—0
A Bulirsch-Stoer algorithm using Gaussian processes—0
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling—0
Bayesian Variational Optimization for Combinatorial Spaces—0
Show:102550
← PrevPage 71 of 197Next →

Benchmark Results

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