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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 1911–1920 of 1963 papers

TitleStatusHype
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes—0
Pseudo-Marginal Bayesian Inference for Gaussian Processes—0
A temporal model of text periodicities using Gaussian Processes—0
Reasoning about Probabilities in Dynamic Systems using Goal Regression—0
Modelling Annotator Bias with Multi-task Gaussian Processes: An Application to Machine Translation Quality Estimation—0
Infinite Mixtures of Multivariate Gaussian Processes—0
Bayesian Structured Prediction Using Gaussian ProcessesCode0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix ApproximationsCode0
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming—0
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Benchmark Results

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