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 1111–1120 of 1963 papers

TitleStatusHype
Sparse Variational Student-t Processes—0
Sparse within Sparse Gaussian Processes using Neighbor Information—0
Sparsifying Suprema of Gaussian Processes—0
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs—0
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows—0
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features—0
Spatiotemporal Besov Priors for Bayesian Inverse Problems—0
Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting—0
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology—0
Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes—0
Show:102550
← PrevPage 112 of 197Next →

Benchmark Results

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