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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 1701–1710 of 1963 papers

TitleStatusHype
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential EquationsCode0
Bayesian Optimization with Tree-structured Dependencies—0
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Robust Bayesian Optimization with Student-t Likelihood—0
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks—0
Location Dependent Dirichlet Processes—0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
Variational Bayesian Multiple Instance Learning With Gaussian ProcessesCode0
Statistical abstraction for multi-scale spatio-temporal systems—0
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Benchmark Results

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