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CALL FOR PAPERS
JCST Special Section on Deep Learning

Introduction
Recent years have observed a number of big breakthroughs in research on artificial intelligence, including self-driving cars, computer Go, image recognition, speech recognition, and machine translation, and in almost all the cases deep learning technologies have played an important role. Indeed, significant progresses have been made in research and development of deep learning and their applications into robotics, computer vision, speech processing, natural language processing, etc. It is not exaggerated to say, therefore, that deep learning is one of the most revolutionary technologies of modern times.

Aims and Scope
The goal of this special section is to publish original and high quality research papers on deep learning and its applications. We call for submissions which represent the state-of-the-art works with regard to the topics. Extended versions of papers published at international conferences, particularly those at top conferences are welcome. Survey papers will be by invitation only.
Topics of interest include, but are not limited to:

  • New models and architectures of deep learning
  • New techniques for deep learning
  • Deep learning theory
  • Systems for deep learning
  • Speech processing using deep learning
  • Natural language processing using deep learning
  • Computer vision using deep learning
  • Search and recommendation using deep learning

Important Dates
Submission: December 20, 2016
First Decision: February 15, 2017
Final Decision: April 15, 2017
Camera-Ready: May 5, 2017
Publication: July 5, 2017

Submission Procedures
All submissions must be done electronically through JCST's e-submission system at https://mc03.manuscriptcentral.com/jcst, with a manuscript type: "Special Section on Deep Learning”.

Guest Editors
Xiang Bai, Department of Electronics and Information Engineering, Huazhong University of Science & Technology, Wuhan
Xuanjing Huang, School of Computer Science, Fudan University, Shanghai
Hang Li, Noah’s Ark Lab, Huawei Technologies, Hong Kong
Changshui Zhang, Department of Automation, Tsinghua University, Beijing

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