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 1231–1240 of 1963 papers

TitleStatusHype
Uncertainty Prediction for Machine Learning Models of Material Properties—0
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations—0
Uncertainty Quantification for Transformer Models for Dark-Pattern Detection—0
Uncertainty Quantification of Darcy Flow through Porous Media using Deep Gaussian Process—0
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality—0
Understanding Probabilistic Sparse Gaussian Process Approximations—0
Unified field theoretical approach to deep and recurrent neuronal networks—0
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control—0
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control—0
Universal low-rank matrix recovery from Pauli measurements—0
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Benchmark Results

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