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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 571–580 of 1963 papers

TitleStatusHype
Bayesian Optimization using Deep Gaussian Processes—0
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature—0
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei—0
Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators—0
Experiment Design with Gaussian Process Regression with Applications to Chance-Constrained Control—0
Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes—0
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes—0
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification—0
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations—0
Application of machine learning to gas flaring—0
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Benchmark Results

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