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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 171–180 of 1963 papers

TitleStatusHype
Physics-Informed Variational State-Space Gaussian ProcessesCode0
Theoretical Analysis of Heteroscedastic Gaussian Processes with Posterior Distributions—0
Amortized Variational Inference for Deep Gaussian Processes—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
Decomposing Gaussians with Unknown CovarianceCode0
Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes—0
Predicting Electricity Consumption with Random Walks on Gaussian Processes—0
Inference for Large Scale Regression Models with Dependent Errors—0
Operator Learning with Gaussian ProcessesCode1
Heartbeat classification using various machine learning models: A comparative studyCode0
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Benchmark Results

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