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 1841–1850 of 1963 papers

TitleStatusHype
MCMC for Variationally Sparse Gaussian Processes—0
Mondrian Forests for Large-Scale Regression when Uncertainty MattersCode0
Provable Bayesian Inference via Particle Mirror Descent—0
String Gaussian Process Kernels—0
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models—0
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningCode1
Optimal change point detection in Gaussian processes—0
Batch Bayesian Optimization via Local PenalizationCode0
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
Necessary and Sufficient Conditions for Surrogate Functions of Pareto Frontiers and Their Synthesis Using Gaussian Processes—0
Show:102550
← PrevPage 185 of 197Next →

Benchmark Results

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