| 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 | 827 | 2018 |
| Don't Decay the Learning Rate, Increase the Batch Size SL Smith, PJ Kindermans, C Ying, QV Le ICLR 2018, 2018 | 789 | 2018 |
| 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 | 527 | 2018 |
| Schnet: A continuous-filter convolutional neural network for modeling quantum interactions K Schütt, PJ Kindermans, HE Sauceda Felix, S Chmiela, A Tkatchenko, ... Advances in neural information processing systems 30, 2017 | 517 | 2017 |
| 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 | 448 | 2016 |
| 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 | 382 | 2019 |
| A benchmark for interpretability methods in deep neural networks S Hooker, D Erhan, PJ Kindermans, B Kim Advances in neural information processing systems 32, 2019 | 358* | 2019 |
| Sign language recognition using convolutional neural networks L Pigou, S Dieleman, PJ Kindermans, B Schrauwen European conference on computer vision, 572-578, 2014 | 356 | 2014 |
| Learning how to explain neural networks: PatternNet and PatternAttribution PJ Kindermans, KT Schuett, M Alber, KR Müller, D Erhan, B Kim, ... ICLR 2018, 2018 | 348* | 2018 |
| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... J. Mach. Learn. Res. 20 (93), 1-8, 2019 | 252 | 2019 |
| Bignas: Scaling up neural architecture search with big single-stage models J Yu, P Jin, H Liu, G Bender, PJ Kindermans, M Tan, T Huang, X Song, ... ECCV, 2020 | 132 | 2020 |
| 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 | 102 | 2014 |
| 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 | 90 | 2016 |
| 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 | 90 | 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 | 89 | 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 | 85 | 2012 |
| Can weight sharing outperform random architecture search? an investigation with tunas G Bender, H Liu, B Chen, G Chu, S Cheng, PJ Kindermans, QV Le Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 81 | 2020 |
| Neural predictor for neural architecture search W Wen, H Liu, H Li, Y Chen, G Bender, PJ Kindermans ECCV, 2020 | 81 | 2020 |
| 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 | 76 | 2012 |
| Mobiledets: Searching for object detection architectures for mobile accelerators Y Xiong, H Liu, S Gupta, B Akin, G Bender, Y Wang, PJ Kindermans, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 51 | 2021 |
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
