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 1201–1210 of 1963 papers

TitleStatusHype
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
Gaussian Process Models of Sound Change in Indo-Aryan Dialectology—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Gaussian Process Morphable Models—0
Gaussian Process Neurons—0
Gaussian Process Neurons Learn Stochastic Activation Functions—0
Gaussian Process on the Product of Directional Manifolds—0
Gaussian Process Optimization with Mutual Information—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
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Benchmark Results

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