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Felix Biessmann
Felix Biessmann
Einstein Center Digital Future, Berlin University of Applied Sciences
Verificeret mail på bht-berlin.de - Startside
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On the interpretation of weight vectors of linear models in multivariate neuroimaging
S Haufe, F Meinecke, K Görgen, S Dähne, JD Haynes, B Blankertz, ...
Neuroimage 87, 96-110, 2014
14002014
Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control
JM Hahne, F Biessmann, N Jiang, H Rehbaum, D Farina, FC Meinecke, ...
Neural Systems and Rehabilitation Engineering, IEEE Transactions on 22 (2 …, 2014
4352014
Transparency and trust in artificial intelligence systems
P Schmidt, F Biessmann, T Teubner
Journal of Decision Systems 29 (4), 260-278, 2020
3082020
Automating large-scale data quality verification
S Schelter, D Lange, P Schmidt, M Celikel, F Biessmann, A Grafberger
Proceedings of the VLDB Endowment 11 (12), 1781-1794, 2018
2722018
On Challenges in Machine Learning Model Management
S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ...
Bulletin of the IEEE Computer Society Technical Committee on Data …, 2018
2332018
A benchmark for data imputation methods
S Jäger, A Allhorn, F Bießmann
Frontiers in big Data 4, 693674, 2021
1972021
Analysis of Multimodal Neuroimaging Data
F Bießmann, S Plis, FC Meinecke, T Eichele, KR Müller
IEEE Reviews in Biomedical Engineering 4, 26 - 58, 2011
1942011
Application scenarios for artificial intelligence in nursing care: rapid review
K Seibert, D Domhoff, D Bruch, M Schulte-Althoff, D Fürstenau, ...
Journal of medical Internet research 23 (11), e26522, 2021
1822021
Decoding Three-Dimensional Trajectory of Executed and Imagined Arm Movements from Electroencephalogram Signals
JH Kim, F Biessmann, SW Lee
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014
1572014
Quantifying Interpretability and Trust in Machine Learning Systems
P Schmidt, F Biessmann
AAAI-19 Workshop on Network Interpretability for Deep Learning, 2019
1432019
DataWig: Missing value imputation for tables
F Biessmann, T Rukat, P Schmidt, P Naidu, S Schelter, A Taptunov, ...
Journal of Machine Learning Research 20 (175), 1-6, 2019
1212019
Temporal kernel CCA and its application in multimodal neuronal data analysis
F Bießmann, FC Meinecke, A Gretton, A Rauch, G Rainer, NK Logothetis, ...
Machine Learning 79, 5-27, 2010
1182010
Learning from more than one data source: data fusion techniques for sensorimotor rhythm-based brain–computer interfaces
S Fazli, S Dähne, W Samek, F Bießmann, KR Müller
Proceedings of the IEEE 103 (6), 891-906, 2015
1122015
Multivariate machine learning methods for fusing multimodal functional neuroimaging data
S Dähne, F Biessmann, W Samek, S Haufe, D Goltz, C Gundlach, ...
Proceedings of the IEEE 103 (9), 1507-1530, 2015
1102015
Deep Learning for Missing Value Imputation in Tables with Non-Numerical Data
F Biessmann, D Salinas, S Schelter, P Schmidt, D Lange
Proceedings of the 27th ACM International Conference on Information and …, 2018
942018
Regularized linear discriminant analysis of EEG features in dementia patients
E Neto, F Biessmann, H Aurlien, H Nordby, T Eichele
Frontiers in aging neuroscience 8, 273, 2016
852016
Effects of stimulus type and of error-correcting code design on BCI speller performance
J Hill, J Farquhar, S Martens, F Bießmann, B Schölkopf
Advances in neural information processing systems 21, 2008
652008
Stereoscopic depth increases intersubject correlations of brain networks
M Gaebler, F Biessmann, JP Lamke, KR Mueller, H Walter, S Hetzer
Neuroimage, 2014
522014
Interactive explainable deep learning model informs prostate cancer diagnosis at MRI
CA Hamm, GL Baumgärtner, F Biessmann, NL Beetz, A Hartenstein, ...
Radiology 307 (4), e222276, 2023
512023
Learning to validate the predictions of black box classifiers on unseen data
S Schelter, T Rukat, F Bießmann
Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020
502020
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