Connectionists: CfP: Mathematical Foundations of Deep Learning in Imaging Science (JMIV Special Issue)

[Joachim Weickert]

                           Call for Papers

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

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  **  MATHEMATICAL FOUNDATIONS OF DEEP LEARNING IN IMAGING SCIENCE   **
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Guest Editors:

  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

Deep learning methods have become an omnipresent and highly successful
part of recent approaches in imaging and vision. However, in most cases
they are used on a purely empirical basis without real understanding of
their behavior. From a scientific viewpoint, this is unsatisfying.

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

Gaining mathematical insights will be the decisive criterion for
inclusion into this special issue. Manuscripts which are primarily
experimental are not eligible.


DEADLINE AND SUBMISSION INSTRUCTIONS

* Deadline for submission:          *************************
                                    **  November 30, 2018  **
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  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.
  There is no page limit.

* 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.

Web site:  http://www.mia.uni-saarland.de/JMIV/si-deep-learning.html

For questions and more information, please contact
Joachim Weickert <weickert at mia.uni-saarland.de>.