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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 941–950 of 1963 papers

TitleStatusHype
Influenza Forecasting Framework based on Gaussian Processes—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation—0
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
Informative Planning and Online Learning with Sparse Gaussian Processes—0
Gaussian Process Latent Class Choice Models—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
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Benchmark Results

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