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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 1481–1490 of 1963 papers

TitleStatusHype
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC—0
Gaussian processes with linear operator inequality constraintsCode0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Performance prediction of data streams on high-performance architecture—0
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler—0
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimizationCode0
Scalable GAM using sparse variational Gaussian processes—0
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
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Benchmark Results

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