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HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks.

Heungseok Park, Yoonsoo Nam, Ji-Hoon Kim

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    |October 13, 2020
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    Summary
    This summary is machine-generated.

    HyperTendril offers a visual analytics system to guide automated machine learning (AutoML) hyperparameter tuning. This approach helps users refine search spaces and configurations for better deep learning model optimization.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning Engineering

    Background:

    • Manual hyperparameter tuning for deep neural networks is complex and time-consuming.
    • Automated Machine Learning (AutoML) methods require careful initial configurations for effective hyperparameter optimization.

    Purpose of the Study:

    • To introduce HyperTendril, a web-based visual analytics system for user-driven hyperparameter tuning.
    • To enable users to interactively steer AutoML processes and gain insights into search algorithm behaviors.

    Main Methods:

    • Developed a model-agnostic visual analytics system, HyperTendril.
    • Implemented an iterative, interactive tuning procedure for refining search spaces and AutoML configurations.
    • Incorporated variable importance analysis for hyperparameter interaction effects.

    Main Results:

    • Users can gain insights into hyperparameter search algorithms and diagnose configurations.
    • Variable importance analysis aids in refining search spaces based on hyperparameter relevance.
    • Evaluation through a longitudinal user study and deployment in an industrial setting.

    Conclusions:

    • HyperTendril empowers users to effectively steer hyperparameter optimization processes.
    • The system facilitates a deeper understanding of AutoML behaviors and improves tuning efficiency.
    • Visual analytics offers a promising approach to enhance human intervention in AutoML.