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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 581–590 of 1963 papers

TitleStatusHype
A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking—0
Understanding Sparse Feature Updates in Deep Networks using Iterative Linearisation—0
Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework—0
Counterfactual Learning with Multioutput Deep KernelsCode0
The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models—0
Challenges in Gaussian Processes for Non Intrusive Load MonitoringCode0
Deep Gaussian Processes for Air Quality InferenceCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Introduction and Exemplars of Uncertainty Decomposition—0
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification—0
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Benchmark Results

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