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 1861–1870 of 1963 papers

TitleStatusHype
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
Regression with Linear Factored Functions—0
Nested Variational Compression in Deep Gaussian Processes—0
Extended and Unscented Gaussian Processes—0
Fast Kernel Learning for Multidimensional Pattern Extrapolation—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Probabilistic Differential Dynamic Programming—0
Two Gaussian Approaches to Black-Box Optomization—0
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Benchmark Results

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