Jörn-Henrik Jacobsen

me
Contact: j.jacobsen [at] vectorinstitute.ai

I am a postdoc at Vector Institute with Rich Zemel, also collaborating with David Duvenaud and Roger Grosse. Previously, I was a postdoc in the lab of Matthias Bethge in Tübingen and a Ph.D. student at the University of Amsterdam under supervision of Arnold Smeulders. My research mainly focuses on gaining better understanding of open challenges in generative modeling, representation learning and robust decision-making. My background is in physics with a specialization in neuroscience.

I have been working on representation learning with generative models, on invertible neural networks and normalizing flows, on generalization under distribution shift as well as on the connection between invariance and adversarial examples.

Publications

W. Grathwohl, K. C. Wang*, J.-H. Jacobsen*, David Duvenaud, Mohammad Norouzi, Kevin Swersky. Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One. Under Submission, 2019. [Paper]

J. Behrmann*, P. Vicol*, K. C. Wang*, Roger Grosse, J.-H. Jacobsen. On the Invertibility of Invertible Neural Networks. NeurIPS workshop on Machine Learning with Guarantees, 2019.

R. T. Q. Chen, J. Behrmann, D. Duvenaud, J.-H. Jacobsen. Residual Flows for Invertible Generative Modeling. NeurIPS, 2019. (SPOTLIGHT) [Paper, Code]

An earlier version appeared in: INNF Workshop, ICML 2019. (CONTRIBUTED TALK) [Link]

Q. Li*, S. Hague*, C. Anil, J. Lucas, R. Grosse, J.-H. Jacobsen. Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. NeurIPS, 2019. [Paper, Code]

E. Fetaya*, J.-H. Jacobsen*, R. Zemel. Conditional Generative Models are not Robust. Under Submission, 2019. [Paper]

J. Behrmann*, W. Grathwohl*, R. T. Q. Chen, D. Duvenaud, J.-H. Jacobsen*. Invertible Residual Networks. ICML, 2019. (LONG ORAL) [Paper, Code]

E. Creager, D. Madras, J.-H. Jacobsen, M. Weis, K. Swersky, T. Pitassi, R. Zemel. Flexibly Fair Representation Learning by Disentanglement. ICML, 2019. [Paper]

J.-H. Jacobsen, J. Behrmann, N. Carlini, F. Tramer, N. Papernot. Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. SafeML Workshop, ICLR 2019. [Paper, Code]

J.-H. Jacobsen, J. Behrmann, R. Zemel, M. Bethge. Excessive Invariance Causes Adversarial Vulnerability. ICLR, 2019. [Paper]

J.-H. Jacobsen, A.W.M. Smeulders, E. Oyallon. i-RevNet: Deep Invertible Networks. ICLR, 2018. [Paper, Code]

J.-H. Jacobsen, B. de Brabandere, A.W.M. Smeulders. Dynamic Steerable Blocks in Deep Residual Networks. BMVC, 2017. [Paper]

J.-H. Jacobsen, E. Oyallon, S. Mallat, A.W.M. Smeulders. Hierarchical Attribute CNNs. ICML PADL, 2017. [Paper, Code]

J.-H. Jacobsen, B. de Brabandere, A.W.M. Smeulders. Dynamic Steerable Frame Networks. Pre-print, 2017. [Paper]

J.-H. Jacobsen, J. v. Gemert, Z. Lou, A.W.M. Smeulders. Structured Receptive Fields in CNNs. CVPR, 2016. [Paper, Code]

J.-H. Jacobsen, A.W.M. Smeulders. Deep Learning for Neuroimage Classification. OHBM, 2015.

J.-H. Jacobsen, J. Stelzer, T. H. Fritz, G. Chételat, R. L. Joie, R. Turner. Why musical memory can be preserved in advanced Alzheimer's disease. BRAIN, 2015. [Paper] [Scientific commentary by Clark and Warren].

On the news: BBC; Science News; Reddit Frontpage; MedicalXpress; The Verge; El Pais; Noisey; Max-Planck-Society; Spiegel Online

R. Turner, J.-H. Jacobsen. What stays when everything goes. OUPblog, 2015. Oxford University Press Blog.

Invited Talks

Deep Learning Summer School Lecture on Unsupervised Learning with Likelihood-based Generative Models - Edmonton, Canada; July 2019
ICML Workshop Invited Talk on Invertible Neural Nets and Normalizing Flows - Long Beach, USA; June 2019
ICML Workshop Invited Talk on Tractable Probabilistic Models - Long Beach, USA; June 2019
Google Brain, Toronto - Toronto, Canada; April 2019
Courant Institute, NYU - New York, USA; March 2019
IAS / U of Princeton CS - Princeton, USA; March 2019
Vector Institute - Toronto, Canada; June 2018
Amsterdam Data Science Deep Dive - Amsterdam, Netherlands; January 2018
Max-Planck-Institute for Intelligent Systems - Tübingen, Germany; September 2017
University of Toronto - Toronto, Canada; September 2017
Google Brain, Toronto - Toronto, Canada; September 2017
University of Oxford: VGG Seminar - Oxford, United Kingdom; September 2017
Facebook AI Research - New York, USA; August 2017
University of Tübingen - Tübingen, Germany; May 2017
ICML Workshop Invited Talk on Data-efficient ML - New York City, USA; June 2016

Reviewing

NeurIPS2019 (Top 50% Reviewer)
ICML2019
ICLR2019
NIPS2018 (Top Reviewer w/ free admission)
CVPR2018 (Best Reviewer)
ICML2018
ICLR2017
NIPS2017
ICML2017
ICLR2016

Jörn-Henrik Jacobsen
Vector Institute
MaRS Centre, West Tower
661 University Ave., Suite 710
Toronto, ON M5G 1M1