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 1091–1100 of 1963 papers

TitleStatusHype
Signal-based Bayesian Seismic Monitoring—0
Simultaneous Twin Kernel Learning using Polynomial Transformations for Structured Prediction—0
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces—0
Sketching the Heat Kernel: Using Gaussian Processes to Embed Data—0
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting—0
Small Sample Spaces for Gaussian Processes—0
Smart Forgetting for Safe Online Learning with Gaussian Processes—0
Solving Dynamic Discrete Choice Models Using Smoothing and Sieve Methods—0
Sparse Convolved Gaussian Processes for Multi-output Regression—0
Sparse Covariance Modeling in High Dimensions with Gaussian Processes—0
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Benchmark Results

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