Recent Advances of Neural Network Optimization and Algorithms in Deep Learning
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 135
Special Issue Editor
Special Issue Information
Dear Colleagues,
In recent years, neural networks, as one of the hottest artificial intelligence technologies, have made breakthroughs and driven the rapid development of science and technology. However, due to the complexity of neural network structures and algorithm optimization, their future development still faces many challenges. The optimization of neural networks is a key factor in improving their performance. This Special Issue aims to demonstrate (1) weight initialization, optimization and regularization of neural network algorithms; and (2) adaptive learning, distributed learning and reinforcement learning in neural networks. The editors hope to collect a number of research studies reporting the recent developments in the related research topics. In addition, researchers can promote their innovative ideas on the topic of recent advances in neural network optimization in the field of deep learning by submitting manuscripts to this Special Issue.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Weight initialization, optimization and regularization in neural network algorithms;
- Adaptive learning, distributed learning and reinforcement learning in neural networks;
- A review of recent advances of neural network optimization and algorithms.
I look forward to receiving your contributions.
Dr. Yang Liu
Guest Editor
Manuscript Submission Information
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Keywords
- neural networks
- optimizer
- regularization
- distributed learning
- reinforcement learning
