Jörn-Henrik Jacobsen
Jörn-Henrik Jacobsen
Apple AI/ML Research
Verified email at - Homepage
Cited by
Cited by
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
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
Invertible residual networks
J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen
International conference on machine learning, 573-582, 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
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
Residual flows for invertible generative modeling
RTQ Chen, J Behrmann, DK Duvenaud, JH Jacobsen
Advances in Neural Information Processing Systems 32, 2019
i-RevNet: Deep Invertible Networks
JH Jacobsen, A Smeulders, E Oyallon
International Conference on Learning Representations (ICLR), 2018
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
Environment inference for invariant learning
E Creager, JH Jacobsen, R Zemel
International Conference on Machine Learning, 2189-2200, 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
Excessive invariance causes adversarial vulnerability
JH Jacobsen, J Behrmann, R Zemel, M Bethge
arXiv preprint arXiv:1811.00401, 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
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
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
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
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
Understanding the Limitations of Conditional Generative Models
E Fetaya, JH Jacobsen, W Grathwohl, R Zemel
arXiv preprint arXiv:1906.01171, 2019
Dynamic steerable blocks in deep residual networks
JH Jacobsen, B De Brabandere, AWM Smeulders
arXiv preprint arXiv:1706.00598, 2017
Hierarchical Attribute CNNs
JH Jacobsen, E Oyallon, S Mallat, AWM Smeulders
ICML 2017 Workshop on Principled Approaches to Deep Learning, 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
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