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

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Showing 751–760 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
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—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
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
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Benchmark Results

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