SOTAVerified

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 1871–1880 of 1963 papers

TitleStatusHype
Entropy of Overcomplete Kernel Dictionaries—0
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions—0
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes—0
Graphical LASSO Based Model Selection for Time Series—0
Joint Emotion Analysis via Multi-task Gaussian Processes—0
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence—0
Approximation errors of online sparsification criteria—0
Optimality of Poisson processes intensity learning with Gaussian processes—0
Probabilistic Network Metrics: Variational Bayesian Network Centrality—0
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models—0
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Benchmark Results

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