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 1371–1380 of 1963 papers

TitleStatusHype
Analysis of Financial Credit Risk Using Machine Learning—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Analytical Results for the Error in Filtering of Gaussian Processes—0
Analytical results for uncertainty propagation through trained machine learning regression models—0
An analytic comparison of regularization methods for Gaussian Processes—0
An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios—0
A New Reliable & Parsimonious Learning Strategy Comprising Two Layers of Gaussian Processes, to Address Inhomogeneous Empirical Correlation Structures—0
A New Representation of Successor Features for Transfer across Dissimilar Environments—0
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection—0
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation—0
Show:102550
← PrevPage 138 of 197Next →

Benchmark Results

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