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 961–970 of 1963 papers

TitleStatusHype
Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes—0
Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks—0
Safety-Critical Learning of Robot Control with Temporal Logic Specifications—0
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
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Benchmark Results

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