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. SchüttBIFOLD, Technische Universität BerlinVerified email at tu-berlin.de
Quoc V. LeResearch Scientist, Google BrainVerified email at stanford.edu
Maximilian AlberTU BerlinVerified email at tu-berlin.de
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andVerified email at donders.ru.nl
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
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
David HübnerAlbert-Ludwigs-Universität FreiburgVerified email at blbt.uni-freiburg.de
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/IAPRCTO and Chief Scientist, National Center for Artificial Intelligence (NCAI), KSAVerified 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	700	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	695	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	445	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	417	2016
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	406	2017
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	341	2019
Sign language recognition using convolutional neural networks L Pigou, S Dieleman, PJ Kindermans, B Schrauwen European Conference on Computer Vision, 572-578, 2014	325	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	310*	2018
A benchmark for interpretability methods in deep neural networks S Hooker, D Erhan, PJ Kindermans, B Kim arXiv preprint arXiv:1806.10758, 2018	274*	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	205	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	98	2014
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	97	2020
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	89	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	87	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
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	81	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	75	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	62	2020
Neural predictor for neural architecture search W Wen, H Liu, H Li, Y Chen, G Bender, PJ Kindermans ECCV, 2020	55	2020
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	40	2017

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