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DL-dashboard: user-friendly deep learning model development environment

Published: 16 March 2019 Publication History

Abstract

In recent years, deep learning has contributed to a big step forward in artificial intelligence, so that deep learning models have been created extensively in a variety of areas. However, development of deep learning model requires high implementation skills as well as domain knowledge. Additionally, finding the best model is a process of a lot of trial-and-error for developers. To alleviate the developers' difficulties, we have developed a deep learning model development environment called DL-Dashboard that allows developers can create new models easily and quickly by drag-and-dropping built-in layer component and can train the models by selecting one of the suggested training options without much deep learning experience. We explain design principles and implementation of DL-Dashboard system and show how developers can create and train models user-friendly on it.

References

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Jeffrey Dunn, Introducing FBLearner Flow: Facebook's AI backbone. https://code.fb.com/core-data/introducing-fblearner-flow-facebook-s-ai-backbone/.
[2]
Martin Abadi et al. 2016. TensorFlow: A System for Large-Scale Machine Learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI'16), Savannah, GA, USA, 265--283.
[3]
Microsoft Azure Machine Learning Studio, https://studio.azureml.net/.
[4]
Shinyoung Ahn et al. 2018. ShmCaffe: Distributed Deep Learning Platforms with Shared Memory Buffer in High Performance Computing, In Proceedings of the 38<sup>th</sup> IEEE International Conference on Distributed Computing Systems(ICDCS'18). IEEE, Vienna, Austria, 1118--1128.
[5]
Yangqing Jia et al. 2014. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22nd ACM international conference on Multimedia (MM'14). ACM, Orlando, FL, USA, 675--678.

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  1. DL-dashboard: user-friendly deep learning model development environment

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    cover image ACM Conferences
    IUI '19 Companion: Companion Proceedings of the 24th International Conference on Intelligent User Interfaces
    March 2019
    173 pages
    ISBN:9781450366731
    DOI:10.1145/3308557
    © 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    New York, NY, United States

    Publication History

    Published: 16 March 2019

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    Author Tags

    1. deep learning
    2. deep learning model creation
    3. user interface

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    • Short-paper

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    • Korea government (MSIT)

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    IUI '19
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    Overall Acceptance Rate 746 of 2,811 submissions, 27%

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