Symmetries and Applications in Machine Learning
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 46
Special Issue Editors
Interests: deep learning; multimodal data fusion; transfer learning
Special Issue Information
Dear Colleagues,
Symmetry is a theoretical concept that originates from physics. It denotes invariance under a set of transformations and is inherently possessed in an image, a physical system, or a text. Such invariant patterns we would like to learn are powerful for constructing an efficient machine learning model. A key insight is that a learning system should reflect these symmetries to be effective. Thereby, this Special Issue concentrates on recent advancements in theoretical aspects or practical applications across widespread domains, such as computer vision, natural language processing, biomedical computing, mechanical engineering, and edge computing, ultimately enabling the creation of more intelligent, reliable, and physically plausible AI systems.
We are pleased to invite you to publish your reviews and regular research articles within this Special Issue, titled “Symmetries and Applications in Machine Learning”. The original research achievements having sufficient experiments and theoretical analysis are highly encouraged to be submitted to this Special Issue.
This Special Issue aims to present and aggregate research achievements that fall into symmetry study in model design, data processing, and algorithm optimization about machine learning or deep neural networks and their applications in biomedicine, mechanics, computer vision, etc.
We look forward to receiving your contributions.
Dr. Nannan Lu
Dr. Yan Chen
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 250 words) can be sent to the Editorial Office for assessment.
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. Symmetry 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.
Keywords
- machine learning
- deep neural networks
- invariance
- data efficiency
- generalization
- representation learning
- optimization
- symmetry
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