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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 961–970 of 1963 papers

TitleStatusHype
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Interpretable deep Gaussian processes with moments—0
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Interrelation of equivariant Gaussian processes and convolutional neural networks—0
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness—0
Intrinsic Bayesian Optimisation on Complex Constrained Domain—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
Gaussian Process Latent Class Choice Models—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces—0
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Benchmark Results

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