JSAIT CFP: Deep Learning: Mathematical Foundations and Applications to Information Science

IEEE JSAIT's inaugural issue will be on Deep Learning: Mathematical Foundations and Applications to Information Science.

This special issue will focus on the mathematical foundations of deep learning as well as applications across information science. Prospective authors are invited to submit original manuscripts on topics within this broad scope including, but not limited to:

Information theoretic methods for deep learning
Robustness for training and inference
Understanding generalization in over-parametrized models
Efficient and compressed model representations
Deep generative models and inverse problems
Large-scale efficient training of large models
Non-convex optimization in deep learning
Deep learning for source and channel coding

Guest Editors

Lead Guest Editor: Alex Dimakis, UT Austin: dimakis@austin.utexas.edu
Richard Baraniuk: Rice University, richb@rice.edu
Sewoong Oh: University of Washington, sewoong@cs.washington.edu
Negar Kiyavash, EPFL: negar.kiyavash@epfl.ch
Rebecca Willett, University of Chicago: willett@uchicago.edu

Submission Guidelines

Prospective authors must follow the IEEE Journal on Selected Areas in Information Theory guidelines regarding the manuscript and its format. For details and templates, please refer to the IEEE Journal on Selected Areas in Information Theory Author Information webpage. All papers should be submitted through Scholar One according to the schedule below.

Important Dates

Manuscript Due: Extended to October 15. No further extensions will be given.
Acceptance Notification: 15 March 2020
Final to Publisher: 5 April 2020
Expected Publication: April/May 2020

Submission site: https://mc.manuscriptcentral.com/jsait-ieee

JSAIT CFP: Deep Learning: Mathematical Foundations and Applications to Information Science

Guest Editors

Submission Guidelines

Important Dates

COVID-19 Updates

Upcoming Events