Jörn-Henrik Jacobsen

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Jens BehrmannApple AI/ML ResearchVerified email at apple.com
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
Will GrathwohlResearch Scientist, DeepmindVerified email at deepmind.com
David DuvenaudAssociate Professor, University of TorontoVerified email at cs.toronto.edu
Matthias BethgeTübingen University & Maddox Co-FounderVerified email at bethgelab.org
Arnold SmeuldersProfessor of Computer Science, University of AmsterdamVerified email at uva.nl
Wieland BrendelFellow at ELLIS Institut Tübingen, Group Leader, Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Robert GeirhosResearch Scientist, Google DeepMindVerified email at google.com
Ricky Tian Qi ChenMeta FAIRVerified email at meta.com
Elliot CreagerUniversity of WaterlooVerified email at uwaterloo.ca
Edouard OyallonCNRS, Sorbonne UniversityVerified email at cnrs.fr
Roger GrosseAssociate Professor, University of TorontoVerified email at cs.toronto.edu
David Scott KruegerUniversity Assistant Professor, University of CambridgeVerified email at cam.ac.uk
Robert TurnerMax Planck Institute for Human Cognitive and Brain Sciences, LeipzigVerified email at cbs.mpg.de
Nicolas PapernotUniversity of Toronto and Vector InstituteVerified email at utoronto.ca
Nicholas CarliniGoogle DeepMindVerified email at google.com
Florian TramèrAssistant Professor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
Ethan FetayaBar-Ilan UniversityVerified email at biu.ac.il
Christina Heinze-DemlApple AI/ML ResearchVerified email at apple.com
Adriel SaportaNew York UniversityVerified email at nyu.edu

Jörn-Henrik Jacobsen

Apple AI/ML Research

Verified email at vectorinstitute.ai - Homepage

Machine Learning Neuroscience Computer Vision


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Shortcut learning in deep neural networks R Geirhos, JH Jacobsen, C Michaelis, R Zemel, W Brendel, M Bethge, ... Nature Machine Intelligence 2 (11), 665-673, 2020	2095	2020
Out-of-distribution generalization via risk extrapolation (rex) D Krueger, E Caballero, JH Jacobsen, A Zhang, J Binas, D Zhang, ... International conference on machine learning, 5815-5826, 2021	931	2021
Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International conference on machine learning, 573-582, 2019	700	2019
Your classifier is secretly an energy based model and you should treat it like one W Grathwohl, KC Wang, JH Jacobsen, D Duvenaud, M Norouzi, ... arXiv preprint arXiv:1912.03263, 2019	619	2019
Why musical memory can be preserved in advanced Alzheimer’s disease JH Jacobsen, J Stelzer, TH Fritz, G Chételat, R La Joie, R Turner Brain: a Journal of Neurology 138 (8), 2438-2450, 2015	427	2015
Residual flows for invertible generative modeling RTQ Chen, J Behrmann, DK Duvenaud, JH Jacobsen Advances in Neural Information Processing Systems 32, 2019	411	2019
Flexibly fair representation learning by disentanglement E Creager, D Madras, JH Jacobsen, M Weis, K Swersky, T Pitassi, ... International conference on machine learning, 1436-1445, 2019	408	2019
i-RevNet: Deep Invertible Networks JH Jacobsen, A Smeulders, E Oyallon International Conference on Learning Representations (ICLR), 2018	407	2018
Environment inference for invariant learning E Creager, JH Jacobsen, R Zemel International Conference on Machine Learning, 2189-2200, 2021	401	2021
How to train your neural ODE: the world of Jacobian and kinetic regularization C Finlay, JH Jacobsen, L Nurbekyan, A Oberman International conference on machine learning, 3154-3164, 2020	317*	2020
Excessive invariance causes adversarial vulnerability JH Jacobsen, J Behrmann, R Zemel, M Bethge arXiv preprint arXiv:1811.00401, 2018	193	2018
Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations F Tramèr, J Behrmann, N Carlini, N Papernot, JH Jacobsen International conference on machine learning, 9561-9571, 2020	141*	2020
Structured Receptive Fields in CNNs JH Jacobsen, J van Gemert, Z Lou, AWM Smeulders The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2610-2619, 2016	131	2016
Understanding and mitigating exploding inverses in invertible neural networks J Behrmann, P Vicol, KC Wang, R Grosse, JH Jacobsen International Conference on Artificial Intelligence and Statistics, 1792-1800, 2021	118	2021
Preventing gradient attenuation in lipschitz constrained convolutional networks Q Li, S Haque, C Anil, J Lucas, RB Grosse, JH Jacobsen Advances in neural information processing systems 32, 2019	112	2019
Learning the stein discrepancy for training and evaluating energy-based models without sampling W Grathwohl, KC Wang, JH Jacobsen, D Duvenaud, R Zemel International Conference on Machine Learning, 3732-3747, 2020	96	2020
Understanding the Limitations of Conditional Generative Models E Fetaya, JH Jacobsen, W Grathwohl, R Zemel arXiv preprint arXiv:1906.01171, 2019	60*	2019
Dynamic steerable blocks in deep residual networks JH Jacobsen, B De Brabandere, AWM Smeulders arXiv preprint arXiv:1706.00598, 2017	28	2017
Hierarchical Attribute CNNs JH Jacobsen, E Oyallon, S Mallat, AWM Smeulders ICML 2017 Workshop on Principled Approaches to Deep Learning, 2017	27*	2017
Joint energy-based models for semi-supervised classification S Zhao, JH Jacobsen, W Grathwohl ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning 1, 2020	19	2020

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