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 371–380 of 1963 papers

TitleStatusHype
Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems—0
Incremental Structure Discovery of Classification via Sequential Monte Carlo—0
Adaptive Basis Function Selection for Computationally Efficient PredictionsCode0
Posterior Covariance Structures in Gaussian Processes—0
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?Code0
Artificial Neural Network and Deep Learning: Fundamentals and Theory—0
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
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Benchmark Results

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