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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 1171–1180 of 1963 papers

TitleStatusHype
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning—0
Splitting Gaussian Process Regression for Streaming Data—0
Gene Regulatory Network Inference with Latent Force Models—0
Detecting Misclassification Errors in Neural Networks with a Gaussian Process ModelCode0
Deep kernel processes—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Predicting the Outputs of Finite Networks Trained with Noisy Gradients—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Stein Variational Gaussian ProcessesCode0
A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers—0
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Benchmark Results

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