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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 1111–1120 of 1963 papers

TitleStatusHype
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspacesCode1
Graph Based Gaussian Processes on Restricted Domains—0
Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes—0
Few-shot Learning for Spatial Regression—0
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning—0
Splitting Gaussian Process Regression for Streaming Data—0
Recyclable Gaussian ProcessesCode1
Gene Regulatory Network Inference with Latent Force Models—0
Detecting Misclassification Errors in Neural Networks with a Gaussian Process ModelCode0
Deep kernel processes—0
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Benchmark Results

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