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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 1501–1510 of 1963 papers

TitleStatusHype
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimizationCode0
Scalable GAM using sparse variational Gaussian processes—0
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes—0
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia LanguageCode0
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion—0
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Benchmark Results

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