Dear Colleagues,

Deep Learning (DL) has revolutionized machine learning, especially in the last decade. In recent years, we have been observing a stunning evolution in computing technologies. As a benefit of this unprecedented development, we are capable of working with very large Neural Networks (NNs), composed of multiple layers (Deep Neural Networks), in many applications, such as object recognition-detection, speech recognition and natural language processing. DL is based on feature learning and data representation. Although many Convolutive Neural Network (CNN) and Recurrent Neural Network (RNN) based algorithms have been proposed, a comprehensive theoretical understanding of DNNs remains to be a major research area. Recently, we have seen an increase in the number of approaches that are based on information-theoretic concepts, such as Mutual Information.

In this Special Issue, we would like to collect papers focusing on both the theory and applications of information-theoretic approaches for Deep Learning. The application areas are diverse and some of them include object tracking/detection, speech recognition, natural language processing, neuroscience, bioinformatics, engineering, finance, astronomy, and Earth and space sciences.

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 submissions that pass pre-check are 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 2600 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.