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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 861–870 of 1963 papers

TitleStatusHype
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry—0
Gauss-Legendre Features for Gaussian Process Regression—0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
Gaussian Process Volatility Model—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
Current Methods for Drug Property Prediction in the Real World—0
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution—0
Hierarchical Gaussian Processes with Wasserstein-2 Kernels—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
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Benchmark Results

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