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 1081–1090 of 1963 papers

TitleStatusHype
Wilsonian Renormalization of Neural Network Gaussian Processes—0
Bayesian Optimization using Deep Gaussian Processes—0
Equivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals—0
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals—0
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data—0
Estimation of Riemannian distances between covariance operators and Gaussian processes—0
Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio—0
Evaluation of Deep Gaussian Processes for Text Classification—0
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems—0
Evaluation of Rarity of Fingerprints in Forensics—0
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Benchmark Results

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