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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 1881–1890 of 1963 papers

TitleStatusHype
On solving Ordinary Differential Equations using Gaussian Processes—0
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes—0
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations—0
Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-ParametersCode0
Variational Gaussian Process State-Space Models—0
Transductive Learning for Multi-Task Copula Processes—0
SHEF-Lite 2.0: Sparse Multi-task Gaussian Processes for Translation Quality Estimation—0
Gaussian Processes for Natural Language Processing—0
Simultaneous Twin Kernel Learning using Polynomial Transformations for Structured Prediction—0
Functional Gaussian processes for regression with linear PDE models—0
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Benchmark Results

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