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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 881–890 of 1963 papers

TitleStatusHype
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—0
Gaussian Process Regression for Maximum Entropy Distribution—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
Gaussian Process Regression constrained by Boundary Value Problems—0
Correcting Model Bias with Sparse Implicit Processes—0
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
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Benchmark Results

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