Pieter-Jan Kindermans
Pieter-Jan Kindermans
Senior Research Scientist, Google Brain
Geverifieerd e-mailadres voor google.com
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Don't Decay the Learning Rate, Increase the Batch Size
SL Smith, PJ Kindermans, C Ying, QV Le
ICLR 2018, 2018
5082018
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
4782018
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
3622016
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
3092018
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ...
arXiv preprint arXiv:1706.08566, 2017
2612017
Sign language recognition using convolutional neural networks
L Pigou, S Dieleman, PJ Kindermans, B Schrauwen
European Conference on Computer Vision, 572-578, 2014
2552014
Learning how to explain neural networks: PatternNet and PatternAttribution
PJ Kindermans, KT Schuett, M Alber, KR Müller, D Erhan, B Kim, ...
ICLR 2018, 2018
238*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
2342019
A benchmark for interpretability methods in deep neural networks
S Hooker, D Erhan, PJ Kindermans, B Kim
arXiv preprint arXiv:1806.10758, 2018
157*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
1242019
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
972014
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
812012
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
782014
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
762014
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
722012
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
702016
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
372020
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
362017
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
362013
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
302017
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Artikelen 1–20