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When less is more: Simplifying inputs aids neural network understanding



Robin Tibor Schirrmeister , Rosanne Liu , Sara Hooker , Tonio Ball


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Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features



Robin Tibor Schirrmeister , Yuxuan Zhou , Tonio Ball , Dan Zhang


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Machine-Learning-Based Diagnostics of EEG Pathology



Lukas Alexander Wilhelm Gemein , Robin Tibor Schirrmeister , Patryk Chrabąszcz , Daniel Wilson , Joschka Boedecker , Andreas Schulze-Bonhage , Frank Hutter , Tonio Ball


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Deep Invertible Networks for EEG-based brain-signal decoding



Robin Tibor Schirrmeister , Tonio Ball


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Intracranial Error Detection via Deep Learning



Martin Völker , Jiří Hammer , Robin T. Schirrmeister , Joos Behncke , Lukas D. J. Fiederer , Andreas Schulze-Bonhage , Petr Marusič , Wolfram Burgard , Tonio Ball

* 8 pages, 6 figures. Accepted at the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018) 

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Training Generative Reversible Networks



Robin Tibor Schirrmeister , Patryk Chrabąszcz , Frank Hutter , Tonio Ball

* Source code for this study is at https://github.com/robintibor/generative-reversible 

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A large-scale evaluation framework for EEG deep learning architectures



Felix A. Heilmeyer , Robin T. Schirrmeister , Lukas D. J. Fiederer , Martin Völker , Joos Behncke , Tonio Ball

* 7 pages, 3 figures, final version accepted for presentation at IEEE SMC 2018 conference 

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