Best Practices in Neural Architecture Search
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 539
Special Issue Editors
Interests: heuristic optimization algorithms; evolutionary algorithms; machine learning; neural networks; multi-agent system
Interests: evolutionary computation; machine learning; web and mobile security
Special Issue Information
Dear Colleagues,
As an emerging topic in the field of machine learning, the success of neural architecture search (NAS) has promoted the deployment of effective and efficient (deep) neural networks in various scenarios, and, thus, has gained increasing popularity in both academia and industry. Nevertheless, some challenges remain unsolved for the practice of NAS, e.g., unbalanced/insufficient datasets, privacy protection issues, limits of computational resources (e.g., device memory, computation time, or energy consumption), and highly specialized tasks (e.g., face recognition, medical severity classification, image segmentation). Therefore, this Special Issue is intended for the presentation of the best practices in NAS, to fill the gap between algorithmic research and representative/original applications.
Areas relevant to the practice of NAS include, but are not limited to, novel NAS algorithms and applications, end-to-end platforms/pipelines, NAS for healthcare, federated NAS, multi-objective NAS, NAS for generative models and applications thereof, physics-informed NAS, and knowledge-driven NAS.
This Special Issue will publish high-quality, original research papers, in the overlapping fields of:
- Deep/Machine learning;
- Reinforcement learning;
- Evolutionary computation;
- Federated learning;
- Neuroevolution;
- Multi-objective optimization;
- Surrogate-assisted optimization;
- Generative learning;
- Auto-machine learning.
Prof. Dr. Giovanni Iacca
Prof. Dr. Eric Medvet
Prof. Dr. Cheng He
Guest Editors
Manuscript Submission Information
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Keywords
- deep/machine learning
- reinforcement learning
- evolutionary computation
- federated learning
- neuroevolution
- multi-objective optimization
- surrogate-assisted optimization
- generative learning
- auto-machine learning
