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 841–850 of 1963 papers

TitleStatusHype
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification—0
Graph Classification Gaussian Processes via Hodgelet Spectral Features—0
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions—0
Graph Convolutional Gaussian Processes For Link Prediction—0
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes—0
Graphical LASSO Based Model Selection for Time Series—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
A Unified Kernel for Neural Network Learning—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
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Benchmark Results

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