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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 1191–1200 of 1963 papers

TitleStatusHype
Gaussian Processes with Differential Privacy—0
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems—0
Gaussian Processes with State-Dependent Noise for Stochastic Control—0
Simultaneous Reconstruction and Uncertainty Quantification for Tomography—0
Gaussian Process for Trajectories—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Gaussian Process Latent Class Choice Models—0
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity—0
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Benchmark Results

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