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Displaying 1 - 2 of 2

Generative Adversarial Network Performance in Low-Dimensional Settings

April 20, 2021

Author(s)

Felix M. Jimenez, Amanda Koepke, Mary Gregg, Michael R. Frey

A generative adversarial network (GAN) is an artificial neural network with a distinctive training architecture, designed to create examples that faithfully reproduce a target distribution. GANs have recently had particular success in applications

Estimating the Parameters of Circles and Ellipses Using Orthogonal Distance Regression and Bayesian Errors-in-Variables Regression

December 5, 2019

Author(s)

Jolene D. Splett, Felix M. Jimenez, Amanda A. Koepke

In ordinary least-squares regression, independent variables are assumed to be known without error. However, in many real-life situations this assumption is not valid. Both orthogonal distance regression and Bayesian errors-in-variables regression can be

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