| Download | - View final version: Phase space sampling and operator confidence with generative adversarial networks (PDF, 910 KiB)
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| Author | Search for: Mills, Kyle1; Search for: Tamblyn, Isaac1 |
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| Affiliation | - National Research Council of Canada. Security and Disruptive Technologies
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| Format | Text, Article |
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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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| In | |
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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 (opens in a new tab) |
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| Record identifier | 062beebf-9bcd-479e-ab33-e1f11174f2cd |
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| Record created | 2017-11-14 |
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| Record modified | 2020-05-30 |
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