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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 1381–1390 of 1963 papers

TitleStatusHype
Band-Limited Gaussian Processes: The Sinc Kernel—0
Coarse-scale PDEs from fine-scale observations via machine learning—0
Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version—0
Finite size corrections for neural network Gaussian processes—0
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal DynamicsCode0
Using Contextual Information to Improve Blood Glucose Prediction—0
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal DimensionCode0
Stochastic data-driven model predictive control using Gaussian processes—0
Gaussian Process Models of Sound Change in Indo-Aryan Dialectology—0
Sequential Learning of Active SubspacesCode0
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Benchmark Results

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