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 1221–1230 of 1963 papers

TitleStatusHype
Likelihood-Free Inference with Deep Gaussian ProcessesCode0
Towards Recurrent Autoregressive Flow Models—0
Real-Time Regression with Dividing Local Gaussian Processes—0
Safety Verification of Unknown Dynamical Systems via Gaussian Process Regression—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm—0
Gaussian Processes on Graphs via Spectral Kernel Learning—0
Uncertainty quantification using martingales for misspecified Gaussian processesCode0
Fast Deep Mixtures of Gaussian Process Experts—0
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions—0
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Benchmark Results

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