Jörn-Henrik Jacobsen

Contact: j.jacobsen [at] vectorinstitute.ai

I am a postdoc at Vector Institute. Previously, I was a postdoc in the lab of Matthias Bethge in Tübingen and a Ph.D. student at the University of Amsterdam. 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.

One question I find particularly intriguing is that we have little knowledge about why Deep Networks perform so well and which properties of the data they use to solve given tasks. I am working on understanding and improving Deep Convolutional Networks by imposing additional structure on them. Carefully structured models can help us to open up the "black-box", so we can draw actual conclusions about what these networks learn, how they learn and when they will fail.


R. T. Q. Chen, J. Behrmann, D. Duvenaud, J.-H. Jacobsen. Residual Flows for Invertible Generative Modeling. INNF Workshop, ICML 2019. (CONTRIBUTED TALK) [Paper]

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 and Presentations


NIPS2018 (Top Reviewer w/ free admission)
CVPR2018 (Best Reviewer)

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