I am a postdoc in the lab of Matthias Bethge in Tübingen. Previously, I was a Ph.D. student at the University of Amsterdam at the crossroads between deep learning, computer vision, and neuroscience, supervised by Arnold Smeulders. My background is in physics with a specialization in neuroscience.
My current research is focused on the problem 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.-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.
Dynamic Steerable Frame Networks - Bert de Brabandere; ESAT, KU Leuven.
Large-scale fMRI Brain analysis - Max-Planck-Institute CBS, Leipzig; Max-Planck-Institute Kyb, Tübingen.
Alzheimer's and Musical Memory Preservation - Prof. Robert Turner; Max-Planck-Institute CBS, Leipzig.
Structured Receptive Field CNNs - Jan v. Gemert, TU Delft.
Centre for Integrative Neuroscience