Computational Methods and Applications of Neural Networks
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 12
Special Issue Editor
Special Issue Information
Dear Colleagues,
The Special Issue aims to explore the latest advancements and innovations in the field of computational methods and applications of neural networks. Recent major neural networks include Mamba-based frameworks. These frameworks excel in speech recognition, time series analysis, and natural language understanding. In addition, Swin Transformer and SegFormer are important models for visual tasks, which enable 3D scene reconstruction and real-time semantic segmentation. Generative models like Stable Diffusion 3, DALL-E 3, and Midjourney support multimodal content creation, from text-to-image synthesis to 3D object generation.
Applications of neural networks exist across diverse fields. However, the escalating scale and complexity of models present unprecedented challenges in computational efficiency, resource consumption, training stability, and deployment feasibility. This Special Issue aims to curate cutting-edge research that addresses these challenges through foundational advances in the computational and algorithmic principles of neural networks. We seek original contributions that introduce novel methods, provide rigorous theoretical analysis, and demonstrate significant improvements in the efficiency, scalability, and capability of neural models.
Considering your expertise in the field, I encourage you to submit an article. If you are unable to contribute at this time, please feel free to share this invitation with your colleagues. Alternatively, you may provide us with the contact details of anyone who might be interested, and we will reach out to them directly.
Thank you for your time, and I look forward to receiving your contributions.
Dr. Wuman Luo
Guest Editor
Manuscript Submission Information
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Keywords
- deep learning
- neural networks
- representation learning
- learning theory
- optimization
- probabilistic methods
- artificial intelligence for sciences
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