Advanced Mathematical Methods for Machine Learning, Neural Networks, and Computer Vision
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 14 June 2026 | Viewed by 322
Special Issue Editor
Special Issue Information
Dear Colleagues,
The integration of mathematical methods in machine learning, neural networks, and computer vision has become increasingly vital as AI applications expand across various industries. These methods provide the theoretical foundation for building reliable, interpretable, and efficient AI systems. Recent advances in geometric deep learning, statistical learning theory, and sparse representation have significantly enhanced the performance of AI systems, yet many theoretical challenges remain to be addressed.
We are pleased to invite you to contribute to this Special Issue, which focuses on the application and theoretical underpinnings of mathematical methods in machine learning, neural networks, and computer vision. This Issue will highlight how mathematical theories drive innovative breakthroughs in AI algorithms and promote the practical application of AI technologies.
This Special Issue aims to collect original research articles and reviews exploring the application of mathematical methods in the aforementioned fields. We particularly encourage submissions on geometric deep learning, sparse representation, optimization algorithms, statistical learning theory, and generative models. These studies should provide theoretical support for AI systems and facilitate their application in practical tasks. Other topics of interest include, but are not limited to, sparse models, low-rank structures, stochastic algorithms, and explainability analysis.
In this Special Issue, original research articles and reviews are welcome. Research areas may include geometric deep learning, sparse representation, optimization algorithms, statistical learning theory, generative models, sparse models, low-rank structures, stochastic algorithms, explainability analysis, etc.
We look forward to receiving your contributions.
Prof. Dr. Guodong Li
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 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. Mathematics is an international peer-reviewed open access semimonthly 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.
Keywords
- mathematical optimization
- geometric deep learning
- neural network analysis
- robust AI design
- machine learning theory
- computer vision modeling
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