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  Using Generative Adversarial Network for learning joint task/response distribution in functional Magnetic Resonance Imaging

Lee, J., Stelzer, J., Loktyushin, A., & Lohmann, G. (2018). Using Generative Adversarial Network for learning joint task/response distribution in functional Magnetic Resonance Imaging. In Workshop for Women in Machine Learning (WiML 2018).

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-80C3-B Version Permalink: https://hdl.handle.net/21.11116/0000-0007-8221-0
Genre: Meeting Abstract

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https://docs.google.com/document/d/1Xm0EIgM9ZPAaX39bbGmkQ-tzzQvWewgmuK4sI2cvgg4/edit (Table of contents)
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 Creators:
Lee, JY1, 2, Author           
Stelzer, J1, 2, Author           
Loktyushin, A1, 2, Author           
Lohmann, G1, 2, Author           
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Dates: 2018-12
 Publication Status: Published online
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Title: Workshop for Women in Machine Learning (WiML 2018) Co-Located with NeurIPS
Place of Event: Montreal, Canada
Start-/End Date: 2018-12-03

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Title: Workshop for Women in Machine Learning (WiML 2018)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 94 Start / End Page: - Identifier: -