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 1321–1330 of 1963 papers

TitleStatusHype
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data—0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Adaptive finite element type decomposition of Gaussian processes—0
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets—0
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models—0
Adaptive Inducing Points Selection For Gaussian Processes—0
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes—0
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches—0
Adaptive Sensing for Learning Nonstationary Environment Models—0
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
Show:102550
← PrevPage 133 of 197Next →

Benchmark Results

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