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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 1591–1600 of 1963 papers

TitleStatusHype
Normative Modeling of Neuroimaging Data using Scalable Multi-Task Gaussian Processes—0
Bayesian approach to model-based extrapolation of nuclear observables—0
Wrapped Gaussian Process Regression on Riemannian Manifolds—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Dirichlet-based Gaussian Processes for Large-scale Calibrated ClassificationCode0
Calibrating Deep Convolutional Gaussian ProcessesCode0
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive VarianceCode0
Efficient Inference in Multi-task Cox Process ModelsCode0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
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Benchmark Results

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