Recent Advances of Computational and Mathematical Applications in Deep Learning

Dear Colleagues,

The Special Issue is aimed toward recent theoretical advances and practical computational and mathematical applications in deep learning. The editors would like to provide an opportunity to present state-of-the-art research on the development of cutting-edge neural network model architectures, the understanding, explainability, and interpretability of deep learning models, their mathematical bias and fairness, robustness,  and ethical and societal regulations and considerations, including but not limited to subjects such as image recognition, retrieval, generation and processing, speech, speaker and emotion recognition and synthesis, style translation, generative adversarial models, reinforcement learning, transfer learning, natural language processing, attention mechanisms, transformer-based networks, sequence modelling, data augmentation, time series analysis, biomedical and agricultural applications, computer vision, etc. The Special Issue will address the following non-exhaustive list of topics (however, we also encourage other ideas in the scope of this issue): 

deep learning theory and applications; computational and mathematical applications (information theory; optimization algorithms; probability and statistics; functional analysis; signal processing; graph theory; medical; pharmaceutical; agricultural; automotive; educational; and financial applications; genomic sequence analysis; gaming; etc.); convolutional neural networks; recurrent neural networks; long short-term memory networks; reinforcement learning; transfer learning; autoencoders; attention mechanisms; generative models; transformers; natural language processing; speech and audio processing; object recognition; style transform; scene understanding; classification and clustering; unsupervised; semi-supervised; supervised; and self-supervised learning; explainable AI. 

We hope that the initiative will be attractive to deep learning researchers and experts in mathematical and computational deep learning applications, and we highly encourage you to submit your current research for peer review before the deadline.

Dr. Branislav Popovic
Dr. Marko Janev
Dr. Lazar Kopanja
Guest Editors

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. Axioms 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 2400 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.

• deep learning
• neural networks
• generative models
• autoencoders
• reinforcement learning
• transfer learning
• style translation
• object recognition
• signal processing
• optimization