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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 761–770 of 1963 papers

TitleStatusHype
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Deep Gaussian Covariance Network—0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Band-Limited Gaussian Processes: The Sinc Kernel—0
Deep Factors with Gaussian Processes for Forecasting—0
Bandits for Learning to Explain from Explanations—0
Deep Ensemble Kernel Learning—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
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Benchmark Results

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