Manuscript Due

June 1, 2019 (in 2 months, 1 week)


As a result of radical advances at the hardware and algorithmic level and due to the increase in the availability of data, the last decade has been marked by the tremendous success of deep learning in various tasks in signal analysis and, more recently, reconstruction and processing. Currently, deep-neural-network models constitute the state of the art in analysis, reconstruction and generative tasks in different applications involving various types of data, including image/video, text, and audio data, networked data (IoT data, social media data), and biomedical and bioinformatics data. A fundamental limiting factor for deep learning is that its theoretical understanding remains underdeveloped, which in turn translates into a lack of principled methods to design such architectures. In response to these challenges, this special issue invites contributions revolving around: the theoretical foundations of deep learning, with particular interest in principles linked or derived from signal processing; the design of interpretable deep neural networks through such foundations; and the application of interpretable deep learning in various tasks in signal processing, reconstruction, generation and analysis.

Lead Guest Editor

  • Nikos Deligiannis, Vrije Universiteit Brussel, Belgium

Guest Editors

  • Saikat Chatterjee, KTH, Sweden
  • Monika Dörfler, University of Vienna, Austria
  • Raja Giryes, Tel Aviv University, Israel
  • Zhanyu Ma, Beijing University of Posts and Telecommunications, China
EURASIP Journal on Advances in Signal Processing