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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 1811–1820 of 1963 papers

TitleStatusHype
Deep Gaussian Processes for Regression using Approximate Expectation Propagation—0
An analytic comparison of regularization methods for Gaussian Processes—0
State Space representation of non-stationary Gaussian Processes—0
Facility Deployment Decisions through Warp Optimizaton of Regressed Gaussian Processes—0
Probabilistic Programming with Gaussian Process Memoization—0
Quantum assisted Gaussian process regression—0
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels—0
Bayesian Model Adaptation for Crowd Counts—0
GP Kernels for Cross-Spectrum Analysis—0
Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection—0
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Benchmark Results

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