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 311–320 of 1963 papers

TitleStatusHype
A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression—0
A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking—0
Active learning of neural response functions with Gaussian processes—0
Asynchronous Distributed Variational Gaussian Processes for Regression—0
Asymmetric kernel in Gaussian Processes for learning target variance—0
A Gaussian Process perspective on Convolutional Neural Networks—0
Correlated Dynamics in Marketing Sensitivities—0
Optimal Privacy-Aware Stochastic Sampling—0
Accelerating ABC methods using Gaussian processes—0
Convergence Rates of Constrained Expected Improvement—0
Show:102550
← PrevPage 32 of 197Next →

Benchmark Results

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