Mathematics of Deep Learning

5 November 2019
TU Delft
4TU Delft
4TU Eindhoven
4TU Twente
4TU Wageningen
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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

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

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Mathematics of Deep Learning

5 November 2019
TU Delft

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

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