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 1101–1125 of 1963 papers

TitleStatusHype
Sparse discovery of differential equations based on multi-fidelity Gaussian process—0
Sparse Gaussian Processes for Stochastic Differential Equations—0
Sparse Gaussian processes using pseudo-inputs—0
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels—0
Sparse Gaussian Processes with Spherical Harmonic Features—0
Sparse Gaussian Processes with Spherical Harmonic Features Revisited—0
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations—0
Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)—0
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning—0
Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting—0
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
Neural variational Data Assimilation with Uncertainty Quantification using SPDE priors—0
Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields—0
Spectral band selection for vegetation properties retrieval using Gaussian processes regression—0
Spectral complexity of deep neural networks—0
Spectral Mixture Kernels for Multi-Output Gaussian Processes—0
Show:102550
← PrevPage 45 of 79Next →

Benchmark Results

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