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dc.contributor.authorOates, Tim
dc.date.accessioned2020-09-18T12:16:21Z
dc.date.available2020-09-18T12:16:21Z
dc.date.issued2019-12-10
dc.identifier.urihttp://hdl.handle.net/10713/13729
dc.descriptionIn this presentation, Dr. Tim Oates will introduce GAN architectures, describe how they are learned, and present a number of example applications, including generating "fake" people. Dr. Oates will also review some recent work done in his lab using GANs to denoise EEG data. Dr. Oates’ research focuses on artificial intelligence, machine learning, robotics, and natural language processing. He recently held a Post-doc position at the MIT Laboratory.en_US
dc.description.urihttps://youtu.be/QbEiqRgDd_Qen_US
dc.language.isoen_USen_US
dc.subjectdenoising EEGen_US
dc.subjectgenerative adversarial networks (GAN)en_US
dc.subject.lcshIndependent component analysis (ICA)en_US
dc.subject.meshElectroencephalographyen_US
dc.subject.meshMachine Learningen_US
dc.titleICTR Enrichment Series: An Introduction to Generative Adversarial Networks with an Application to Denoising EEGen_US
dc.typePoster/Presentationen_US
dc.typeVideoen_US


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