Special Issue "The Role of Signal Processing and Information Theory in Modern Machine Learning"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Prof. Dr. Nariman Farsad
Website
Guest Editor
1: Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
2: Department of Computer Science, Ryerson University, Toronto, ON M5B 2K3 Canada
Interests: Machine learning; signal processing; communication systems; data science
Prof. Dr. Marco Mondelli
Website
Guest Editor
Institute of Science and Technology Austria, 3400 Klosterneuburg, Austria
Interests: Information theory; machine learning; data science; wireless communication systems; coding theory
Dr. Morteza Mardani
Website
Guest Editor
Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
Interests: Machine learning, statistical signal processing, artificial intelligence, medical imaging

Special Issue Information

Dear Colleagues,

Breakthroughs in modern machine learning are rapidly changing science, industry, and society, yet fundamental understanding in this area has lagged behind. For example, one of the central tenets of the field, the bias–variance trade-off, appears to be at odds with the observed behavior of methods used in practice and the black-box nature of deep neural network architectures defies explanation. As these technologies are integrated more and more deeply into devices and services used by millions of people worldwide, there is an urgent need to provide theoretical guarantees for machine-learning techniques and to explain why and how these techniques work, based on empirical observation.

Recently, powerful tools from signal processing, information theory, and statistical mechanics have provided insight into the inner workings of modern machine learning. This Special Issue aims to be a forum for the presentation of new and improved techniques at the intersection of Signal Processing, Information Theory, Statistical Mechanics, and Machine Learning. In particular, the theory of deep learning, novel uses of signal processing and information theory in machine learning, explainable deep learning, as well as active and adversarial learning fall within the scope of this Special Issue. 

Prof. Nariman Farsad
Prof. Marco Mondelli
Dr. Morteza Mardani
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Theory of Deep Learning
  • Information Theory in Machine Learning
  • Signal Processing in Machine Learning
  • Active Learning
  • Explainable Deep Learning
  • Adversarial Learning
  • Distributed Machine Learning
  • Statistics
  • Optimization

Published Papers

This special issue is now open for submission.
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