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 641–650 of 1963 papers

TitleStatusHype
Extrinsic Bayesian Optimizations on Manifolds—0
Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes—0
Facility Deployment Decisions through Warp Optimizaton of Regressed Gaussian Processes—0
Fairness-aware Bayes optimal functional classification—0
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning—0
Fast Adaptation with Linearized Neural Networks—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes—0
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations—0
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Benchmark Results

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