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    ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods 

    Schlegel, Udo; Cakmak, Eren; Keim, Daniel A. (The Eurographics Association, 2020)
    Explainable artificial intelligence (XAI) methods aim to reveal the non-transparent decision-making mechanisms of black-box models. The evaluation of insight generated by such XAI methods remains challenging as the applied ...
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    ViNNPruner: Visual Interactive Pruning for Deep Learning 

    Schlegel, Udo; Schiegg, Samuel; Keim, Daniel A. (The Eurographics Association, 2022)
    Neural networks grow vastly in size to tackle more sophisticated tasks. In many cases, such large networks are not deployable on particular hardware and need to be reduced in size. Pruning techniques help to shrink deep ...

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    Author
    Keim, Daniel A. (2)
    Cakmak, Eren (1)Schiegg, Samuel (1)Schlegel, Udo (1)Schlegel, Udo (1)SubjectComputing methodologies (2)Artificial intelligence (1)CCS Concepts: Human-centered computing --> Visual analytics; Computing methodologies --> Neural networks (1)centered computing (1)concepts and models (1)HCI theory (1)Human (1)Human centered computing (1)Neural networks (1)Visual analytics (1)... View MoreDate Issued2020 (1)2022 (1)Has File(s)true (2)

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    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
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