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 1711–1720 of 1963 papers

TitleStatusHype
Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data—0
Large Linear Multi-output Gaussian Process LearningCode0
Efficient Modeling of Latent Information in Supervised Learning using Gaussian ProcessesCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Doubly Stochastic Variational Inference for Deep Gaussian ProcessesCode0
Learning of Gaussian Processes in Distributed and Communication Limited Systems—0
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering—0
Entropic Trace Estimates for Log DeterminantsCode0
Asynchronous Distributed Variational Gaussian Processes for Regression—0
Time Series Prediction for Graphs in Kernel and Dissimilarity SpacesCode0
Show:102550
← PrevPage 172 of 197Next →

Benchmark Results

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