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 651–660 of 1963 papers

TitleStatusHype
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data—0
Visual Pursuit Control based on Gaussian Processes with Switched Motion TrajectoriesCode0
Quantum Bayesian Computation—0
Dynamic Bayesian Learning for Spatiotemporal Mechanistic ModelsCode0
Gaussian Process Surrogate Models for Neural Networks—0
Bayesian Optimization with Informative Covariance—0
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs—0
Data-Driven Stochastic AC-OPF using Gaussian ProcessesCode0
Correcting Model Bias with Sparse Implicit Processes—0
Kullback-Leibler and Renyi divergences in reproducing kernel Hilbert space and Gaussian process settings—0
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Benchmark Results

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