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 1271–1280 of 1963 papers

TitleStatusHype
Bayesian Circular Regression with von Mises Quasi-Processes—0
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes—0
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference—0
Wasserstein Barycenter Gaussian Process based Bayesian Optimization—0
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference—0
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes—0
What's Wrong With That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution—0
Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes—0
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models—0
Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training—0
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Benchmark Results

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