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Pieter-Jan Kindermans
Pieter-Jan Kindermans
Staff Research Scientist, Google Deepmind
Verified email at google.com
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
15812018
Don't Decay the Learning Rate, Increase the Batch Size
SL Smith, PJ Kindermans, C Ying, QV Le
ICLR 2018, 2018
11532018
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
10432017
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
8072018
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
703*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
6622019
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
5532016
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
5102015
Learning how to explain neural networks: PatternNet and PatternAttribution
PJ Kindermans, KT Schuett, M Alber, KR Müller, D Erhan, B Kim, ...
ICLR 2018, 2018
436*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
3822019
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
2742020
Neural predictor for neural architecture search
W Wen, H Liu, H Li, Y Chen, G Bender, PJ Kindermans
ECCV, 2020
1942020
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
1842022
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
1442021
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
1432020
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
1342016
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
1112014
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
992014
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
992014
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
902012
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