Symmetry/Asymmetry in Neural Networks
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 3199
Special Issue Editors
Interests: neural networks; stochastic system; intelligent system; robotics
Interests: control and optimization; source seeking; nonlinear system; multi-agent systems; formation control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the rapidly evolving field of Artificial Intelligence, the concepts of symmetry and asymmetry in neural networks have garnered significant attention. These concepts play a crucial role in the design, function, and performance of various types of neural networks, including static neural networks (SNNs), recurrent neural networks (RNNs), and deep learning architectures.
Symmetry in neural networks often relates to the architecture's ability to respond identically to identical inputs, regardless of their orientation or position, enhancing the network's generalization capabilities. Conversely, introducing asymmetry, whether in data representations, network architectures, or learning algorithms, can lead to a more specialized and efficient processing of complex and variable data sets.
Dr. Yufeng Tian
Dr. Zhenghong Jin
Guest Editors
Manuscript Submission Information
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Keywords
- neural network model
- stability analysis of neural networks
- state estimation on neural networks
- neural networks and deep learning
- neural networks and their applications in robotics
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