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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 431–440 of 1963 papers

TitleStatusHype
A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics—0
Architectures and random properties of symplectic quantum circuits—0
Random ReLU Neural Networks as Non-Gaussian Processes—0
Motion Prediction with Gaussian Processes for Safe Human-Robot Interaction in Virtual Environments—0
Spectral complexity of deep neural networks—0
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes—0
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes—0
Wilsonian Renormalization of Neural Network Gaussian Processes—0
Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data—0
Dynamic Online Ensembles of Basis ExpansionsCode0
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Benchmark Results

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