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 1721–1730 of 1963 papers

TitleStatusHype
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
Fast Design Space Exploration of Nonlinear Systems: Part I—0
Fast emulation of density functional theory simulations using approximate Gaussian processes—0
Faster Kernel Interpolation for Gaussian Processes—0
Faster variational inducing input Gaussian process classification—0
Fast Gaussian Processes under Monotonicity Constraints—0
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation—0
Fast Gaussian Process Regression for Big Data—0
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Benchmark Results

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