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 1041–1050 of 1963 papers

TitleStatusHype
Sample-efficient reinforcement learning using deep Gaussian processes—0
Sample Path Regularity of Gaussian Processes from the Covariance Kernel—0
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks—0
Scalable Bayesian Transformed Gaussian Processes—0
Scalable GAM using sparse variational Gaussian processes—0
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces—0
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors—0
Scalable Gaussian Processes for Supervised Hashing—0
Scalable Gaussian Processes on Discrete Domains—0
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)—0
Show:102550
← PrevPage 105 of 197Next →

Benchmark Results

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