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 under supervision of Arnold Smeulders. My research mainly focuses on gaining better understanding of open challenges in representation learning and neural 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.
J. Behrmann, W. Grathwohl, R. T. Q. Chen, D. Duvenaud, J.-H. Jacobsen. Invertible Residual Networks. Under submission, 2019. [Paper]
J.-H. Jacobsen, J. Behrmann, R. Zemel, M. Bethge. Excessive Invariance Causes Adversarial Vulnerability. ICLR, 2019. [Paper]
J.-H. Jacobsen, B. de Brabandere, A.W.M. Smeulders. Dynamic Steerable Blocks in Deep Residual Networks. BMVC, 2017. [Paper]
J.-H. Jacobsen, B. de Brabandere, A.W.M. Smeulders. Dynamic Steerable Frame Networks. Pre-print, 2017. [Paper]
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].
R. Turner, J.-H. Jacobsen. What stays when everything goes. OUPblog, 2015. Oxford University Press Blog.
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