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    This study reviews combining neural architecture search (NAS) and continual learning (CL) to create adaptive deep neural networks (DNNs). This approach aims for autonomous, lifelong learning systems, reducing manual design and maintenance needs.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Deep neural network (DNN) design is manual, time-consuming, and error-prone.
    • Deployed models often lack adaptiveness to changing environments and require frequent maintenance.
    • Limited accessibility post-deployment in domains like IoT and autonomous vehicles necessitates automated solutions.

    Purpose of the Study:

    • To conduct the first extensive review on the intersection of Neural Architecture Search (NAS) and Continual Learning (CL).
    • To formalize a prospective paradigm for combining NAS and CL.
    • To outline research directions for developing lifelong autonomous DNNs.

    Main Methods:

    • Literature review focusing on the synergy between NAS and CL.
    • Analysis of existing approaches and their limitations.
    • Identification of key challenges and opportunities at the intersection of NAS and CL.

    Main Results:

    • Established the foundational concepts for integrating NAS and CL.
    • Highlighted the potential for creating more robust and adaptive AI agents.
    • Identified critical research gaps and future directions for autonomous DNNs.

    Conclusions:

    • Combining NAS and CL offers a promising path towards automated, adaptive, and lifelong learning DNNs.
    • This integration can significantly reduce manual design efforts and post-deployment maintenance.
    • Further research is needed to fully realize the potential of this combined paradigm for real-world applications.