Special Issue "Transparency of Deep Neural Networks"

A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).

Deadline for manuscript submissions: closed (15 September 2018).

Special Issue Editor

Prof. Dr. Yoichi Hayashi
Website1 Website2
Guest Editor
Artificial Intelligence Lab, Department of Computer Science, Meiji University, Kawasaki, Kanagawa 214-8571, Japan
Interests: artificial intelligence; deep learning; classification; rule extraction; big data analytics; interpretability of deep neural network
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning is a new branch of machine learning which has been proven to be a powerful feature extraction tool in computer vision. The primary disadvantage of deep learning is that it has no clear declarative representation of knowledge. In addition, deep learning has considerable difficulties in generating the necessary explanation structures, which limits its potential because the ability to provide detailed characterizations of classification strategies would promote its acceptance. However, surprisingly, very little work has been carried out in relation to the transparecncy of deep learning. Bridging this gap could be expected to contribute to the real-world utility of deep learning. The transparency of deep neural networks is the first step towards filling this gap. The next step towards utilizing deep neural networks is rule extraction from deep neural networks. Transparency and rule extraction from deep neural networks, therefore, remain areas in need of further innovation.

Prof. Dr. Yoichi Hayashi
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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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 1000 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.


  • Big Data analytics using deep learning
  • Explanation of deep learning
  • Machine learning applied to transparency of deep learning
  • Transparency of deep learning for medical, financial, and industrial big data
  • Accuracy-interpretability dilemma in deep learning

Published Papers

There is no accepted submissions to this special issue at this moment.
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