Neural Architecture Search (NAS)
- Submission status
- Open
- Submission deadline
Neural Architecture Search (NAS) is an emerging area focused on automating the design of neural network architectures. The overarching goal of NAS research is to discover optimal network architectures that can achieve high performance for specific tasks, such as image recognition or natural language processing, without requiring extensive human expertise. Researchers aim to develop algorithms that can efficiently explore the vast space of possible architectures, identify the most promising ones, and refine them to enhance their performance. The current landscape of NAS is characterized by a variety of approaches, including reinforcement learning, evolutionary algorithms, and gradient-based methods. Developments in NAS are oriented towards reducing the computational cost of the search process, improving the generalizability of the existing architectures, and exploring architectures for trending domains such as edge computing and neural network compression.
This Collection presents original research highlighting efforts in creating more efficient and scalable NAS algorithms that can adapt to different tasks and hardware constraints.
Editors
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Snigdhansu Chatterjee, PhD
University of Maryland, USA
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Vasileios Maroulas, PhD
University of Tennessee Knoxville, USA
Collections articles undergo Scientific Reports' standard peer review process and are subject to all of the journal’s standard policies. This includes the journal’s policy on competing interests. The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests. For more information, refer to our Collections guidelines.
This Collection has not been supported by sponsorship.
