Phase space sampling and operator confidence with generative adversarial networks

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TypeArticle
Journal titleCondensed Matter
Article numberarXiv:1710.08053
Pages# of pages: 6
AbstractWe demonstrate that a generative adversarial network can be trained to produce Ising model configurations in distinct regions of phase space. In training a generative adversarial network, the discriminator neural network becomes very good a discerning examples from the training set and examples from the testing set. We demonstrate that this ability can be used as an anomaly detector, producing estimations of operator values along with a confidence in the prediction.
Publication date
PublisherCornell University Library
Linkhttps://arxiv.org/abs/1710.08053
LanguageEnglish
AffiliationSecurity and Disruptive Technologies; National Research Council Canada
Peer reviewedNo
NPARC number23002457
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Record identifier062beebf-9bcd-479e-ab33-e1f11174f2cd
Record created2017-11-14
Record modified2017-11-14
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