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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 1901–1910 of 1963 papers

TitleStatusHype
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes—0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
Infinite attention: NNGP and NTK for deep attention networks—0
Infinite-channel deep stable convolutional neural networks—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes—0
Infinite Mixtures of Multivariate Gaussian Processes—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
Influenza Forecasting Framework based on Gaussian Processes—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
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Benchmark Results

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