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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 1491–1500 of 1963 papers

TitleStatusHype
Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes—0
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia LanguageCode0
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion—0
Physics-Based Learning for Robotic Environmental Sensing—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
The Limitations of Model Uncertainty in Adversarial Settings—0
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
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Benchmark Results

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