## Meeting on Mathematics of Deep Learning

Location: TU Delft, Science Centre

Date: 5 November 2019

Organization committee: Remco Duits, Arnold Heemink, Johannes Schmidt-Hieber, Willem Kruijer

Presentations:

**Estimation ability of deep learning with connection to sparse estimation in function space**

Taiji Suzuki – Department of Mathematical Informatics, University of Tokyo

**Learned SVD - Deep Learning Decomposition for Inverse Problems **

Christoph Brune – Department of Applied Mathematics, University of Twente

**Functional Process Priors for CNNs and VAEs**** **

Max Welling, Institute of Informatics, University of Amsterdam

**Deep limits of residual neural networks**

Yves van Gennip, Delft Institute of Applied Mathematics, Delft University of Technology

**Group Equivariant CNNs beyond Roto-Translations: **** B-Spline CNNs on Lie Groups**

Erik Bekkers, Department of Mathematics and Computer Science, Eindhoven University of Technology

**Implicit bias and regularization in machine learning**

Lorenzo Rosasco, Laboratory for Computational and Statistical Leaning, Massachusetts Institute of Technology.

**Diffusion Variational Autoencoders**

Jim Portegies, Department of Mathematics and Computer Science, Eindhoven University of Technology

**Approximation with sparsely connected deep networks**

Remi Gribonval, Centre de Recherche INRIA Rennes

**Gauge Equivariant Convolutional Networks**

Taco Cohen, Institute of Informatics, University of Amsterdam

**PDE-based CNNs with Morphological Convolutions**** **

Bart Smets, Department of Mathematics and Computer Science, Eindhoven University of Technology

Photografer: Marc Blommaert