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Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
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When Meta-Learning Meets Online and Continual Learning: A Survey.

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    This paper surveys meta-learning, continual learning, and online learning frameworks. It clarifies complex terminology and problem settings to aid researchers in advancing these "learning to learn" approaches.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Deep neural networks have achieved success with mini-batch stochastic gradient descent on large datasets.
    • Research is expanding neural network applications into new learning scenarios like meta-learning, continual learning, and online learning.
    • These frameworks, focused on optimizing learning algorithms and incremental model updates with streaming data, were initially developed separately.

    Purpose of the Study:

    • To provide a comprehensive survey of meta-learning, continual learning, and online learning.
    • To organize diverse problem settings within these frameworks using consistent terminology and formal descriptions.
    • To clarify the distinctions and relationships between these advanced machine learning paradigms.

    Main Methods:

    • Systematic review and organization of existing literature on meta-learning, continual learning, and online learning.
    • Development of unified terminology and formal descriptions for problem settings.
    • Analysis of the combinations and intersections of these learning frameworks.

    Main Results:

    • A structured overview categorizing various problem settings within meta-learning, continual learning, and online learning.
    • Clarification of the complexities and distinctions between these learning paradigms.
    • Identification of recent research investigating the combinations of these frameworks.

    Conclusions:

    • The paper facilitates a clearer understanding of complex learning frameworks by providing consistent terminology and formal descriptions.
    • This organized overview aims to reduce confusion and encourage further research and advancements in the field.
    • By fostering clarity, the work supports the development of novel problem settings and learning algorithms in meta-learning, continual learning, and online learning.