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 1951–1963 of 1963 papers

TitleStatusHype
Twin gaussian processes for structured prediction—0
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes—0
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features—0
Bayesian estimation of orientation preference maps—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
Modeling human function learning with Gaussian processes—0
Variational Mixture of Gaussian Process Experts—0
Bayesian Kernel Shaping for Learning Control—0
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes—0
Sparse Convolved Gaussian Processes for Multi-output Regression—0
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes—0
Robust Regression with Twinned Gaussian Processes—0
Sparse Gaussian processes using pseudo-inputs—0
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Benchmark Results

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