SOTAVerified

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 1801–1810 of 1963 papers

TitleStatusHype
Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes—0
A flexible state space model for learning nonlinear dynamical systems—0
Structured and Efficient Variational Deep Learning with Matrix Gaussian PosteriorsCode0
Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining—0
The Multivariate Generalised von Mises distribution: Inference and applications—0
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution—0
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
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Benchmark Results

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