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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 741–750 of 1963 papers

TitleStatusHype
A computationally lightweight safe learning algorithm—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Gaussian Processes for Music Audio Modelling and Content Analysis—0
Gaussian Processes for Natural Language Processing—0
Gaussian Processes for Nonlinear Signal Processing—0
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations—0
Gaussian Processes for Survival Analysis—0
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
Gaussian Processes indexed on the symmetric group: prediction and learning—0
Efficient Global Optimization using Deep Gaussian Processes—0
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Benchmark Results

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