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.-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.
Benchmarking Neural Decision Making - Matthias Bethge; Bethge Lab, Tübingen.
Dynamic Steerable Frame Networks - Bert de Brabandere; ESAT, KU Leuven.
Alzheimer's and Musical Memory Preservation - Prof. Robert Turner; Max-Planck-Institute CBS, Leipzig.
Structured Receptive Field CNNs - Jan v. Gemert, TU Delft.
MaRS Centre, West Tower
661 University Ave., Suite 710
Toronto, ON M5G 1M1