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 1771–1780 of 1963 papers

TitleStatusHype
Gaussian Process-Based Nonlinear Moving Horizon Estimation—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
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis—0
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences—0
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces—0
Gaussian processes based data augmentation and expected signature for time series classification—0
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Benchmark Results

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