Call for Papers

Special Issue of the Journal of Mathematical Imaging and Vision (JMIV) on the

Mathematical Foundations of Deep Learning in Imaging Science

• Joan Bruna (New York University, USA)
• Eldad Haber (University of British Columbia, Vancouver, Canada)
• Gitta Kutyniok (TU Berlin, Germany)
• Thomas Pock (Graz University of Technology, Austria)
• Rene Vidal (Johns Hopkins University, Baltimore, USA)

Topics of Interest

Many mathematically inclined researchers have a strong desire to understand the theoretical reasons for the success of these approaches and to find relations between deep learning and mathematically well-established techniques in imaging science. The goal of this special issue is to showcase their latest research results and to promote future research in this direction.

Topics of interest include, but are not limited to:

• gaining mathematical introspection into the behavior of deep learning methods, e.g. through
  • theoretical insights into their expressive power, quality, stability, and efficiency
  • analysis of their ability to handle the curse of dimensionality
  • investigation of their generalization properties
  • theoretical bounds on their necessary complexity
  • theories for architectural design
  • characterization of their loss surface
  • analysis of optimization algorithms
  • mathematical theories for generative adversarial networks
• establishing connections between deep learning and successful mathematical concepts in image analysis such as
  • radial basis functions, splines, and harmonic analysis
  • sparsity, compressed sensing, and dictionary learning
  • subspace methods
  • inverse problems, regularization theory, and operator learning
  • variational methods, optimization, and optimal control
  • ordinary and partial differential equations
  • information theory, information geometry, and the physics of information
  • statistical learning theory

Deadline and Submission Instructions

• Deadline for submission: December 31, 2018 (extended).
  The printed special issue will appear in 2019. We aim at fast and efficient reviewing, starting immediately after paper submission. Since accepted manuscripts will become available direcly as online first articles, earlier submission is helpful and encouraged. If an individual manuscript requires substantially more time than the others, it will be published in a regular JMIV issue.
• The usual JMIV submission guidelines apply. All submissions will be peer reviewed according to the JMIV standards. Manuscripts that extend conference papers must contain at least 30 % novel material.
• Submissions must be uploaded through the regular login site (Editorial Manager) of JMIV:
  http://www.editorialmanager.com/jmiv/default.aspx
• Please make sure to choose the "Mathematical Foundations of Deep Learning in Imaging Science" Special Issue after logging in to the JMIV editorial manager. This guarantees that your submission will be assigned to the above guest editors.