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 751–775 of 1963 papers

TitleStatusHype
A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
Gaussian Processes on Cellular Complexes—0
Gaussian Processes on Distributions based on Regularized Optimal Transport—0
Gaussian Processes on Graphs via Spectral Kernel Learning—0
Gaussian Processes on Hypergraphs—0
Gaussian Processes Over Graphs—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
Gaussian Processes with Context-Supported Priors for Active Object Localization—0
Gaussian Processes with Differential Privacy—0
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation—0
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems—0
Gaussian Processes with State-Dependent Noise for Stochastic Control—0
Simultaneous Reconstruction and Uncertainty Quantification for Tomography—0
Gaussian Process for Trajectories—0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—0
Attentive Gaussian processes for probabilistic time-series generation—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Gaussian Process Latent Class Choice Models—0
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity—0
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Show:102550
← PrevPage 31 of 79Next →

Benchmark Results

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