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 1141–1150 of 1963 papers

TitleStatusHype
Frequency-domain Gaussian Process Models for H_ Uncertainties—0
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery—0
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes—0
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Functional Causal Bayesian Optimization—0
Functional Gaussian processes for regression with linear PDE models—0
Functional Priors for Bayesian Neural Networks through Wasserstein Distance Minimization to Gaussian Processes—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
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Benchmark Results

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