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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 571–580 of 1963 papers

TitleStatusHype
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processesCode0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
How to turn your camera into a perfect pinhole model—0
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes—0
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian ManifoldsCode0
A spectrum of physics-informed Gaussian processes for regression in engineering—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Convolutional Deep Kernel MachinesCode0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
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Benchmark Results

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