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 1181–1190 of 1963 papers

TitleStatusHype
Infinite attention: NNGP and NTK for deep attention networks—0
Towards Recurrent Autoregressive Flow Models—0
Matérn Gaussian processes on Riemannian manifoldsCode1
70 years of machine learning in geoscience in reviewCode1
Real-Time Regression with Dividing Local Gaussian Processes—0
Safety Verification of Unknown Dynamical Systems via Gaussian Process Regression—0
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
Gaussian Processes on Graphs via Spectral Kernel Learning—0
Uncertainty quantification using martingales for misspecified Gaussian processesCode0
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Benchmark Results

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