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 721–730 of 1963 papers

TitleStatusHype
Gaussian Processes with Context-Supported Priors for Active Object Localization—0
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles—0
Gaussian process based nonlinear latent structure discovery in multivariate spike train data—0
Gaussian Process-Based Nonlinear Moving Horizon Estimation—0
A Perspective on Gaussian Processes for Earth Observation—0
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer—0
Gaussian Process Conditional Density Estimation—0
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations—0
Gaussian Process Convolutional Dictionary Learning—0
Bayesian Variational Optimization for Combinatorial Spaces—0
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Benchmark Results

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