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 301–310 of 1963 papers

TitleStatusHype
Attentive Gaussian processes for probabilistic time-series generation—0
A General Framework for Fair Regression—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
Bayesian Deconditional Kernel Mean Embeddings—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
Contextual Combinatorial Multi-output GP Bandits with Group Constraints—0
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots—0
A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation—0
A Gaussian Process Regression Model for Distribution Inputs—0
A temporal model of text periodicities using Gaussian Processes—0
Show:102550
← PrevPage 31 of 197Next →

Benchmark Results

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