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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 771–780 of 1963 papers

TitleStatusHype
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity—0
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
Efficient Global Optimization using Deep Gaussian Processes—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
Gaussian Process Neurons—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
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Benchmark Results

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