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 621–630 of 1963 papers

TitleStatusHype
Representing and Learning Functions Invariant Under Crystallographic Groups—0
Training-Free Neural Active Learning with Initialization-Robustness GuaranteesCode0
Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes—0
Graph Classification Gaussian Processes via Spectral Features—0
Global universal approximation of functional input maps on weighted spacesCode0
Taylorformer: Probabilistic Modelling for Random Processes including Time SeriesCode0
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical ModelsCode0
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks—0
Gaussian Processes with State-Dependent Noise for Stochastic Control—0
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Benchmark Results

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