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Meeting Abstract

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

MPS-Authors
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Lee,  JY
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Stelzer,  J
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Loktyushin,  A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Lohmann,  G
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (public)

WiML-2018-Lee-JY.pdf
(Any fulltext), 2MB

Supplementary Material (public)
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Citation

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).


Cite as: http://hdl.handle.net/21.11116/0000-0007-80C3-B
Abstract
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