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 851–860 of 1963 papers

TitleStatusHype
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions—0
Group Importance Sampling for Particle Filtering and MCMC—0
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
A Unified Kernel for Neural Network Learning—0
Harmonizable mixture kernels with variational Fourier features—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes—0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
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Benchmark Results

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