Applications of Symmetry/Asymmetry and Machine Learning
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 967
Special Issue Editor
Special Issue Information
Dear Colleagues,
Symmetry and asymmetry are fundamental concepts that underpin a wide range of natural and artificial systems. In the era of data-driven discovery, the integration of these principles with machine learning has opened new avenues for both theoretical exploration and practical innovation. This Special Issue aims to bring together recent advances at the intersection of symmetry/asymmetry and machine learning, demonstrating how structural regularities and irregularities can be harnessed to improve generalization, robustness, and interpretability. Topics of interest include, but are not limited to, symmetric model architecture design/application, asymmetry-aware feature learning, group-equivariant neural networks and variants, partial multilabel learning, symmetry breaking in optimization, and applications across computer vision, bioinformatics, biometrics, signal processing, and beyond. By highlighting diverse methodologies and applications, this issue seeks to provide a comprehensive perspective on how symmetry-related insights can inspire novel machine learning solutions and deepen our understanding of complex systems.
Dr. Jianhang Zhou
Guest Editor
Manuscript Submission Information
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Keywords
- symmetry in machine learning
- asymmetry-aware learning
- group-equivariant neural networks
- partial multi-label learning
- interpretable machine learning
- robust machine learning
- applications with symmetric model
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