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    Visual Ensemble Analysis to Study the Influence of Hyper-parameters on Training Deep Neural Networks

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    Date
    2019
    Author
    Hamid, Sagad
    Derstroff, Adrian
    Klemm, Sören
    Ngo, Quynh Quang
    Jiang, Xiaoyi
    Linsen, Lars
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    Abstract
    A good deep neural network design allows for efficient training and high accuracy. The training step requires a suitable choice of several hyper-parameters. Limited knowledge exists on how the hyper-parameters impact the training process, what is the interplay of multiple hyper-parameters, and what is the interrelation of hyper-parameters and network topology. In this paper, we present a structured analysis towards these goals by investigating an ensemble of training runs.We propose a visual ensemble analysis based on hyper-parameter space visualizations, performance visualizations, and visualizing correlations of topological structures. As a proof of concept, we apply our approach to deep convolutional neural networks.
    BibTeX
    @inproceedings {vis.20191160,
    booktitle = {Machine Learning Methods in Visualisation for Big Data},
    editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko},
    title = {{Visual Ensemble Analysis to Study the Influence of Hyper-parameters on Training Deep Neural Networks}},
    author = {Hamid, Sagad and Derstroff, Adrian and Klemm, Sören and Ngo, Quynh Quang and Jiang, Xiaoyi and Linsen, Lars},
    year = {2019},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-089-5},
    DOI = {10.2312/mlvis.20191160}
    }
    URI
    https://doi.org/10.2312/mlvis.20191160
    https://diglib.eg.org:443/handle/10.2312/mlvis20191160
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    Eurographics Association copyright © 2013 - 2020 
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    Theme by @mire NV
    System hosted at  Graz University of Technology.
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