| Don't Decay the Learning Rate, Increase the Batch Size SL Smith, PJ Kindermans, C Ying, QV Le ICLR 2018, 2018 | 467 | 2018 |
| SchNet–A deep learning architecture for molecules and materials KT Schütt, HE Sauceda, PJ Kindermans, A Tkatchenko, KR Müller The Journal of Chemical Physics 148 (24), 241722, 2018 | 405 | 2018 |
| Deep dynamic neural networks for multimodal gesture segmentation and recognition D Wu, L Pigou, PJ Kindermans, NDH Le, L Shao, J Dambre, JM Odobez IEEE transactions on pattern analysis and machine intelligence 38 (8), 1583-1597, 2016 | 333 | 2016 |
| Understanding and simplifying one-shot architecture search GM Bender, P Kindermans, B Zoph, V Vasudevan, Q Le International Conference on Machine Learning (ICML) 2018, 2018 | 267 | 2018 |
| Sign language recognition using convolutional neural networks L Pigou, S Dieleman, PJ Kindermans, B Schrauwen European Conference on Computer Vision, 572-578, 2014 | 233 | 2014 |
| Schnet: A continuous-filter convolutional neural network for modeling quantum interactions K Schütt, PJ Kindermans, HES Felix, S Chmiela, A Tkatchenko, KR Müller Advances in neural information processing systems, 991-1001, 2017 | 228 | 2017 |
| Learning how to explain neural networks: PatternNet and PatternAttribution PJ Kindermans, KT Schuett, M Alber, KR Müller, D Erhan, B Kim, ... ICLR 2018, 2018 | 214* | 2018 |
| The (un) reliability of saliency methods PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ... Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 267-280, 2019 | 205 | 2019 |
| A benchmark for interpretability methods in deep neural networks S Hooker, D Erhan, PJ Kindermans, B Kim Advances in Neural Information Processing Systems, 9737-9748, 2019 | 115* | 2019 |
| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... J. Mach. Learn. Res. 20 (93), 1-8, 2019 | 103 | 2019 |
| Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller PJ Kindermans, M Tangermann, KR Müller, B Schrauwen Journal of neural engineering 11 (3), 035005, 2014 | 95 | 2014 |
| A bayesian model for exploiting application constraints to enable unsupervised training of a P300-based BCI PJ Kindermans, D Verstraeten, B Schrauwen PloS one 7 (4), e33758, 2012 | 81 | 2012 |
| True zero-training brain-computer interfacing–an online study PJ Kindermans, M Schreuder, B Schrauwen, KR Müller, M Tangermann PloS one 9 (7), e102504, 2014 | 78 | 2014 |
| Performance measurement for brain–computer or brain–machine interfaces: a tutorial DE Thompson, LR Quitadamo, L Mainardi, S Gao, PJ Kindermans, ... Journal of neural engineering 11 (3), 035001, 2014 | 75 | 2014 |
| A P300 BCI for the masses: prior information enables instant unsupervised spelling PJ Kindermans, H Verschore, D Verstraeten, B Schrauwen Advances In Neural Information Processing Systems 25, 2012 | 72 | 2012 |
| Investigating the influence of noise and distractors on the interpretation of neural networks PJ Kindermans, K Schütt, KR Müller, S Dähne arXiv preprint arXiv:1611.07270, 2016 | 60 | 2016 |
| A unified probabilistic approach to improve spelling in an event-related potential-based brain–computer interface PJ Kindermans, H Verschore, B Schrauwen IEEE Transactions on Biomedical Engineering 60 (10), 2696-2705, 2013 | 37 | 2013 |
| Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees D Hübner, T Verhoeven, K Schmid, KR Müller, M Tangermann, ... PloS one 12 (4), e0175856, 2017 | 33 | 2017 |
| Towards improved design and evaluation of epileptic seizure predictors I Korshunova, PJ Kindermans, J Degrave, T Verhoeven, BH Brinkmann, ... IEEE Transactions on Biomedical Engineering 65 (3), 502-510, 2017 | 28 | 2017 |
| Towards a symbiotic brain–computer interface: exploring the application–decoder interaction T Verhoeven, P Buteneers, JR Wiersema, J Dambre, PJ Kindermans Journal of neural engineering 12 (6), 066027, 2015 | 27 | 2015 |
Klaus-Robert MüllerGoogle Brain (on leave of Professor for Machine Learning, TU Berlin & Korea University, Seoul)Verified email at tu-berlin.de
