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 1231–1240 of 1963 papers

TitleStatusHype
Multi-Fidelity High-Order Gaussian Processes for Physical SimulationCode0
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder—0
All your loss are belong to BayesCode0
Physics Informed Deep Kernel Learning—0
Learning supported Model Predictive Control for Tracking of Periodic References—0
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian ProcessesCode0
Regret Bound for Safe Gaussian Process Bandit Optimization—0
Smart Forgetting for Safe Online Learning with Gaussian Processes—0
Learning Inconsistent Preferences with Gaussian Processes—0
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—0
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Benchmark Results

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