| Download | - View full text: Phase space sampling and operator confidence with generative adversarial networks (PDF, 909 KB)
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| Author | Search for: Mills, Kyle1; Search for: Tamblyn, Isaac1 |
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| Name affiliation | - National Research Council Canada. Security and Disruptive Technologies
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| Format | Text |
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| Type | Article |
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| Journal title | Condensed Matter |
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| Article number | arXiv:1710.08053 |
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| Pages | # of pages: 6 |
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| Abstract | We 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. |
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| Publication date | 2017-10-23 |
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| Publisher | Cornell University Library |
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| Language | English |
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| Peer reviewed | No |
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| NPARC number | 23002457 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction |
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Record identifier | 062beebf-9bcd-479e-ab33-e1f11174f2cd | | Record created | 2017-11-14 |
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| Record modified | 2020-03-16 |
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