Pieter-Jan Kindermans

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Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified 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 TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andVerified email at donders.ru.nl
Quoc V. LeResearch Scientist, Google BrainVerified email at stanford.edu
Maximilian AlberTU BerlinVerified email at tu-berlin.de
Sven DähneAmazonVerified email at amazon.com
Thibault VerhoevenPhD, Ghent UniversityVerified email at ugent.be
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 Shao, Fellow of IEEE/IAPRCEO & Chief Scientist, Inception Institute of Artificial Intelligence (IIAI)Verified email at inceptioniai.org
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	575	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	544	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	387	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	372	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	318	2017
Sign language recognition using convolutional neural networks L Pigou, S Dieleman, PJ Kindermans, B Schrauwen European Conference on Computer Vision, 572-578, 2014	297	2014
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	278	2019
Learning how to explain neural networks: PatternNet and PatternAttribution PJ Kindermans, KT Schuett, M Alber, KR Müller, D Erhan, B Kim, ... ICLR 2018, 2018	264*	2018
A benchmark for interpretability methods in deep neural networks S Hooker, D Erhan, PJ Kindermans, B Kim arXiv preprint arXiv:1806.10758, 2018	200*	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	156	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	96	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	84	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	83	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	79	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	77	2016
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	74	2012
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	57	2020
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	43	2020
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	39	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	36	2017

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