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 871–880 of 1963 papers

TitleStatusHype
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical ApplicationsCode0
Global Convolutional Neural ProcessesCode0
Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep—0
Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models—0
A theory of representation learning gives a deep generalisation of kernel methods—0
Neural Network Gaussian Processes by Increasing DepthCode0
Approximate Bayesian Optimisation for Neural Networks—0
Estimation of Riemannian distances between covariance operators and Gaussian processes—0
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc ExplainabilityCode0
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots—0
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Benchmark Results

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