Mathematical Methods in Machine Learning 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: 31 July 2025 | Viewed by 144
Special Issue Editor
Special Issue Information
Dear Colleagues,
During the recent five years, there has been a surge in the application of mathematical methods to advance the frontiers of machine learning and computer vision. This Special Issue aims to provide a comprehensive overview of these developments, focusing on integrating mathematical and statistical tools with machine learning and computer vision techniques to solve complex problems in various scientific domains.
The topics of interest for publication in this Special Issue include but are not limited to, the exploration of AI-for-science models, such as Physics-Informed Neural Networks (PINNs), Kolmogorov-Arnold Networks (KANs), Optical Neural Networks (ONNs), for approximating solutions to mathematical formulae such as PDEs, discovering physical laws, solving geometry problems, and advancing AI reasoning.
In addition, this Special Issue will highlight the latest advancements in mathematical modeling for computer vision tasks. A typical example is the latest generative Denoising Diffusion Probabilistic Models (DDPMs), which can be interpreted as stochastic differential equations in continuous space. We welcome articles regarding theories and applications of these advanced models which contribute to understanding and improving current deep learning methodologies, especially those large models with an enormous number of parameters.
Overall, this Special Issue aims to present a broad range of research articles demonstrating the power and potential of mathematical methods in driving forward the frontiers of machine learning and computer vision for scientific applications.
Dr. Chenyou Fan
Guest Editor
Manuscript Submission Information
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Keywords
- AI for science
- approximate physical formulae
- approximate mathematical equations
- denoising diffusion probabilistic models
- explainable large language models
- explainable large visual models
- ordinary and partial differential equations
- stochastic differential equations
- ML models for geometry problems
- AI reasoning models
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