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 | 1285 | 2014 |
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 | 406 | 2014 |
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 | 228 | 2018 |
Transparency and trust in artificial intelligence systems P Schmidt, F Biessmann, T Teubner Journal of Decision Systems 29 (4), 260-278, 2020 | 209 | 2020 |
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 | 204 | 2018 |
Analysis of Multimodal Neuroimaging Data F Bießmann, S Plis, FC Meinecke, T Eichele, KR Müller IEEE Reviews in Biomedical Engineering 4, 26 - 58, 2011 | 184 | 2011 |
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 | 145 | 2014 |
Quantifying Interpretability and Trust in Machine Learning Systems P Schmidt, F Biessmann AAAI-19 Workshop on Network Interpretability for Deep Learning, 2019 | 127 | 2019 |
A benchmark for data imputation methods S Jäger, A Allhorn, F Bießmann Frontiers in big Data 4, 693674, 2021 | 122 | 2021 |
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 | 117 | 2021 |
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 | 116 | 2010 |
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 | 109 | 2019 |
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 | 105 | 2015 |
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 | 105 | 2015 |
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 | 87 | 2018 |
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 | 81 | 2016 |
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 | 65 | 2008 |
Stereoscopic depth increases intersubject correlations of brain networks M Gaebler, F Biessmann, JP Lamke, KR Mueller, H Walter, S Hetzer Neuroimage, 2014 | 50 | 2014 |
Simultaneous and proportional control of 2D wrist movements with myoelectric signals JM Hahne, H Rehbaum, F Biessmann, FC Meinecke, KR Müller, N Jiang, ... 2012 IEEE international workshop on machine learning for signal processing, 1-6, 2012 | 48 | 2012 |
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 | 45 | 2020 |