Klaus-Robert Müller

Technische Universität Berlin, Berlin, Berlin, Germany 
Machine Learning
"Klaus-Robert Müller"
Mean distance: 14.34 (cluster 29)
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Hägele M, Seegerer P, Lapuschkin S, et al. (2020) Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. Scientific Reports. 10: 6423
Sattler F, Wiedemann S, Muller KR, et al. (2019) Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data. Ieee Transactions On Neural Networks and Learning Systems
Wiedemann S, Muller KR, Samek W. (2019) Compact and Computationally Efficient Representation of Deep Neural Networks. Ieee Transactions On Neural Networks and Learning Systems
Lapuschkin S, Wäldchen S, Binder A, et al. (2019) Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications. 10: 1096
Horst F, Lapuschkin S, Samek W, et al. (2019) Explaining the unique nature of individual gait patterns with deep learning. Scientific Reports. 9: 2391
Sannelli C, Vidaurre C, Müller KR, et al. (2019) A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity. Plos One. 14: e0207351
Shin J, Kim DW, Müller KR, et al. (2018) Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses. Sensors (Basel, Switzerland). 18
Shin J, Müller KR, Hwang HJ. (2018) Eyes-closed hybrid brain-computer interface employing frontal brain activation. Plos One. 13: e0196359
Shin J, von Lühmann A, Kim DW, et al. (2018) Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset. Scientific Data. 5: 180003
von Luhmann A, Muller KR. (2018) Why build an integrated EEG-NIRS? About the advantages of hybrid bio-acquisition hardware. Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference. 2017: 4475-4478
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