Pieter-Jan Kindermans

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Klaus-Robert MüllerTU Berlin & Korea University & Google Deepmind & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Kristof T. SchüttPfizer, Machine Learning ResearchVerified email at pfizer.com
Benjamin SchrauwenProfessor of Machine Learning, Ghent UniversityVerified email at ugent.be
Quoc V. LeResearch Scientist, GoogleVerified email at stanford.edu
Maximilian AlberTU BerlinVerified email at tu-berlin.de
Dumitru ErhanDirector of Research @ Google DeepMindVerified email at google.com
Sven DähneAmazonVerified email at amazon.com
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andVerified email at donders.ru.nl
Thibault VerhoevenPhD, Ghent UniversityVerified email at ugent.be
Huziel E. SaucedaAssistant Professor, Instituto de Física, Universidad Nacional Autonoma de MexicoVerified email at fisica.unam.mx
Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, TU Berlin; APS FellowVerified email at uni.lu
Joni DambreFull professor, Ghent UniversityVerified email at ugent.be
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
Di WuCovariant.aiVerified email at covariant.ai
Ling Shao, Fellow of IEEE/IAPRGeneral Terminus Technologies; Former CEO of IIAI, Initiator/Provost & EVP of MBZUAIVerified 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

Pieter-Jan Kindermans

Staff Research Scientist, Google Deepmind

Verified email at google.com

Machine Learning Deep Learning Probabilistic Models Unsupervised Learning Brain-Computer


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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), 2018	1468	2018
Don't Decay the Learning Rate, Increase the Batch Size SL Smith, PJ Kindermans, C Ying, QV Le ICLR 2018, 2018	1099	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	947	2017
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	773	2018
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	639*	2019
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	623	2019
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	535	2016
Sign language recognition using convolutional neural networks L Pigou, S Dieleman, PJ Kindermans, B Schrauwen Computer Vision-ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and …, 2015	488	2015
Learning how to explain neural networks: PatternNet and PatternAttribution PJ Kindermans, KT Schuett, M Alber, KR Müller, D Erhan, B Kim, ... ICLR 2018, 2018	422*	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	366	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	252	2020
Neural predictor for neural architecture search W Wen, H Liu, H Li, Y Chen, G Bender, PJ Kindermans ECCV, 2020	179	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	139	2020
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	131	2021
Phenaki: Variable length video generation from open domain textual description R Villegas, M Babaeizadeh, PJ Kindermans, H Moraldo, H Zhang, ... arXiv preprint arXiv:2210.02399, 2022	127	2022
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	124	2016
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	110	2014
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	97	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	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	89	2012

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