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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 521–530 of 1963 papers

TitleStatusHype
Bayesian Structured Prediction Using Gaussian ProcessesCode0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Additive Gaussian Processes RevisitedCode0
Explainable Learning with Gaussian ProcessesCode0
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Benchmark Results

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