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 1021–1030 of 1963 papers

TitleStatusHype
Two Gaussian Approaches to Black-Box Optomization—0
Fast Deep Mixtures of Gaussian Process Experts—0
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes—0
Uncertainty-aware Remaining Useful Life predictor—0
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data—0
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning—0
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference—0
Uncertainty Prediction for Machine Learning Models of Material Properties—0
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations—0
Uncertainty Quantification for Transformer Models for Dark-Pattern Detection—0
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Benchmark Results

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