Pieter-Jan Kindermans

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Klaus-Robert MüllerTU Berlin & Korea University & MPII & Google BrainVerified email at tu-berlin.de
Benjamin SchrauwenProfessor of Machine Learning, Ghent UniversityVerified email at ugent.be
Kristof T. SchüttTechnische Universität BerlinVerified email at tu-berlin.de
Michael TangermannBrain State Decoding Lab and Autonomous Intelligent Systems Lab, University of Freiburg, GermanyVerified email at blbt.uni-freiburg.de
Quoc V. LeResearch Scientist, Google BrainVerified email at stanford.edu
Maximilian AlberTU BerlinVerified email at tu-berlin.de
Thibault VerhoevenPhD, Ghent UniversityVerified email at ugent.be
Sven DähneAmazonVerified email at amazon.com
Joni DambreHoofddocent, Ghent UniversityVerified email at ugent.be
Dumitru ErhanStaff Research Scientist, Google BrainVerified email at google.com
David HübnerAlbert-Ludwigs-Universität FreiburgVerified email at blbt.uni-freiburg.de
Huziel E. SaucedaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, Berlin Big Data CenterVerified email at uni.lu
Lionel PigouGhent UniversityVerified email at ugent.be
Jean-Marc OdobezHead of the Perception and Activity Understanding group, Idiap Research Institute and EPFLVerified email at idiap.ch
Ling ShaoCEO & Chief Scientist, Inception Institute of Artificial Intelligence (IIAI); EVP & Provost, MBZUAIVerified email at uea.ac.uk
Sander DielemanResearch Scientist, DeepMindVerified email at google.com
Jonas DegraveResearch Scientist, DeepmindVerified email at google.com
Martijn SchreuderANT NeuroVerified email at ant-neuro.com
David ThompsonAssistant Professor of Electrical and Computer Engineering, Kansas State UniversityVerified email at ksu.edu

Pieter-Jan Kindermans

Senior Research Scientist, Google Brain

Verified email at google.com

Machine Learning Deep Learning Probabilistic Models Unsupervised Learning Brain-Computer Interfaces


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Don't Decay the Learning Rate, Increase the Batch Size SL Smith, PJ Kindermans, C Ying, QV Le ICLR 2018, 2018	508	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	476	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	363	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	308	2018
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	260	2017
Sign language recognition using convolutional neural networks L Pigou, S Dieleman, PJ Kindermans, B Schrauwen European Conference on Computer Vision, 572-578, 2014	257	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	236*	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	231	2019
A benchmark for interpretability methods in deep neural networks S Hooker, D Erhan, PJ Kindermans, B Kim arXiv preprint arXiv:1806.10758, 2018	155*	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	124	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	97	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	76	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	70	2016
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	36	2020
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	36	2017
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	36	2013
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	30	2017

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