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 1031–1040 of 1963 papers

TitleStatusHype
Structured Machine Learning Tools for Modelling Characteristics of Guided Waves—0
Using BART to Perform Pareto Optimization and Quantify its Uncertainties—0
Gauss-Legendre Features for Gaussian Process Regression—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Deep Ensemble Kernel Learning—0
Optimal Designs of Gaussian Processes with Budgets for Hyperparameter Optimization—0
Time Series Counterfactual Inference with Hidden Confounders—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
Activation-level uncertainty in deep neural networks—0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
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Benchmark Results

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