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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Long-tailed data presents a significant challenge in machine learning due to a large number of minority classes with substantial combined influence.
    • Long-tailed learning (LTL) is an emerging research area focused on developing models that perform accurately across all classes, especially tail classes.

    Purpose of the Study:

    • To provide a comprehensive survey of recent advancements in long-tailed visual learning.
    • To introduce a novel taxonomy for categorizing LTL methods.
    • To systematically review and analyze existing LTL techniques.

    Main Methods:

    • A new taxonomy for LTL is proposed, encompassing eight dimensions: data balancing, neural architecture, feature enrichment, logits adjustment, loss function, bells and whistles, network optimization, and posthoc processing.
    • A systematic review of LTL methods is conducted based on the proposed taxonomy.
    • The distinctions between general imbalance learning and LTL are analyzed.

    Main Results:

    • The survey categorizes various LTL techniques within the established eight-dimensional taxonomy.
    • Commonalities and differences among LTL methods are discussed.
    • Key differences between imbalance learning and LTL are highlighted.

    Conclusions:

    • The comprehensive review and taxonomy offer a structured understanding of the LTL field.
    • Future prospects and research directions in long-tailed visual learning are identified.