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 341–350 of 1963 papers

TitleStatusHype
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring—0
Learning about a changing state—0
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological imagesCode0
Time-changed normalizing flows for accurate SDE modeling—0
Sample Path Regularity of Gaussian Processes from the Covariance Kernel—0
Longitudinal prediction of DNA methylation to forecast epigenetic outcomesCode0
Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes—0
Domain Invariant Learning for Gaussian Processes and Bayesian ExplorationCode0
Frequency-domain Gaussian Process Models for H_ Uncertainties—0
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Benchmark Results

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