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 671–680 of 1963 papers

TitleStatusHype
Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)—0
Additive Gaussian Processes RevisitedCode0
Shallow and Deep Nonparametric Convolutions for Gaussian ProcessesCode0
LIMO: Latent Inceptionism for Targeted Molecule GenerationCode1
On Integrating Prior Knowledge into Gaussian Processes for Prognostic Health Monitoring—0
Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian ProcessesCode0
Deep Variational Implicit ProcessesCode0
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Benchmark Results

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