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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 631–640 of 1963 papers

TitleStatusHype
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning—0
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models—0
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded VarianceCode0
Dynamic Term Structure Models with Nonlinearities using Gaussian Processes—0
Quantum neural networks form Gaussian processes—0
Meta-models for transfer learning in source localisation—0
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification—0
Representing Additive Gaussian Processes by Sparse Matrices—0
A Chain Rule for the Expected Suprema of Bernoulli Processes—0
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Benchmark Results

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