Visualization System for Evolutionary Neural Networks for Deep Learning

Junghoon Chae; Catherine D. Schuman; Steven R. Young; J. Travis Johnston; Derek C. Rose; Robert M. Patton; Thomas E. Potok

doi:10.1109/BigData47090.2019.9006470

Visualization System for Evolutionary Neural Networks for Deep Learning

Junghoon Chae, Catherine D. Schuman, Steven R. Young, J. Travis Johnston, Derek C. Rose, Robert M. Patton, Thomas E. Potok

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Deep learning is actively used in a wide range of fields for scientific discovery. To effectively apply deep learning to a particular problem, it is important to select an appropriate network architecture and other hyper-parameters (at each layer). Evolving architectures and hyper-parameters using a genetic algorithm is one current approach to search the huge space of all possible configurations to find those more optimal for the problem. However, examining an evolutionary process and tuning the genetic algorithm are challenging, pushing most users to treat the process as a black box. To address this challenge, we propose a visualization system for evolutionary neural networks for deep learning. The key feature of our visualization system is to provide a visual analytics environment for evaluating a genetic algorithm in order to improve the underlying operations to reduce time to find good solutions. Our system is able to not only visualize how a genetic algorithm traverses its search space but also allows users to examine evolving networks in-depth to get insights to improve performance through interactive visualization components.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4498-4502
Number of pages	5
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9006470
State	Published - Dec 2019
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: Dec 9 2019 → Dec 12 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	12/9/19 → 12/12/19

Funding

Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

evolutionary algorithm
neural network
visualization

Access to Document

10.1109/BigData47090.2019.9006470

Cite this

Chae, J., Schuman, C. D., Young, S. R., Johnston, J. T., Rose, D. C., Patton, R. M., & Potok, T. E. (2019). Visualization System for Evolutionary Neural Networks for Deep Learning. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 4498-4502). Article 9006470 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006470

Chae, Junghoon ; Schuman, Catherine D. ; Young, Steven R. et al. / Visualization System for Evolutionary Neural Networks for Deep Learning. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 4498-4502 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

@inproceedings{6645c3e2f8bc4b18af786bf43037b584,

title = "Visualization System for Evolutionary Neural Networks for Deep Learning",

abstract = "Deep learning is actively used in a wide range of fields for scientific discovery. To effectively apply deep learning to a particular problem, it is important to select an appropriate network architecture and other hyper-parameters (at each layer). Evolving architectures and hyper-parameters using a genetic algorithm is one current approach to search the huge space of all possible configurations to find those more optimal for the problem. However, examining an evolutionary process and tuning the genetic algorithm are challenging, pushing most users to treat the process as a black box. To address this challenge, we propose a visualization system for evolutionary neural networks for deep learning. The key feature of our visualization system is to provide a visual analytics environment for evaluating a genetic algorithm in order to improve the underlying operations to reduce time to find good solutions. Our system is able to not only visualize how a genetic algorithm traverses its search space but also allows users to examine evolving networks in-depth to get insights to improve performance through interactive visualization components.",

keywords = "evolutionary algorithm, neural network, visualization",

author = "Junghoon Chae and Schuman, {Catherine D.} and Young, {Steven R.} and Johnston, {J. Travis} and Rose, {Derek C.} and Patton, {Robert M.} and Potok, {Thomas E.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9006470",

language = "English",

series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4498--4502",

editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",

booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

}

Chae, J, Schuman, CD, Young, SR, Johnston, JT, Rose, DC , Patton, RM & Potok, TE 2019, Visualization System for Evolutionary Neural Networks for Deep Learning. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9006470, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 4498-4502, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 12/9/19. https://doi.org/10.1109/BigData47090.2019.9006470

Visualization System for Evolutionary Neural Networks for Deep Learning. / Chae, Junghoon; Schuman, Catherine D.; Young, Steven R. et al.
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 4498-4502 9006470 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Visualization System for Evolutionary Neural Networks for Deep Learning

AU - Chae, Junghoon

AU - Schuman, Catherine D.

AU - Young, Steven R.

AU - Johnston, J. Travis

AU - Rose, Derek C.

AU - Patton, Robert M.

AU - Potok, Thomas E.

PY - 2019/12

Y1 - 2019/12

N2 - Deep learning is actively used in a wide range of fields for scientific discovery. To effectively apply deep learning to a particular problem, it is important to select an appropriate network architecture and other hyper-parameters (at each layer). Evolving architectures and hyper-parameters using a genetic algorithm is one current approach to search the huge space of all possible configurations to find those more optimal for the problem. However, examining an evolutionary process and tuning the genetic algorithm are challenging, pushing most users to treat the process as a black box. To address this challenge, we propose a visualization system for evolutionary neural networks for deep learning. The key feature of our visualization system is to provide a visual analytics environment for evaluating a genetic algorithm in order to improve the underlying operations to reduce time to find good solutions. Our system is able to not only visualize how a genetic algorithm traverses its search space but also allows users to examine evolving networks in-depth to get insights to improve performance through interactive visualization components.

AB - Deep learning is actively used in a wide range of fields for scientific discovery. To effectively apply deep learning to a particular problem, it is important to select an appropriate network architecture and other hyper-parameters (at each layer). Evolving architectures and hyper-parameters using a genetic algorithm is one current approach to search the huge space of all possible configurations to find those more optimal for the problem. However, examining an evolutionary process and tuning the genetic algorithm are challenging, pushing most users to treat the process as a black box. To address this challenge, we propose a visualization system for evolutionary neural networks for deep learning. The key feature of our visualization system is to provide a visual analytics environment for evaluating a genetic algorithm in order to improve the underlying operations to reduce time to find good solutions. Our system is able to not only visualize how a genetic algorithm traverses its search space but also allows users to examine evolving networks in-depth to get insights to improve performance through interactive visualization components.

KW - evolutionary algorithm

KW - neural network

KW - visualization

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DO - 10.1109/BigData47090.2019.9006470

M3 - Conference contribution

AN - SCOPUS:85081376517

T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

SP - 4498

EP - 4502

BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

Chae J, Schuman CD, Young SR, Johnston JT, Rose DC , Patton RM et al. Visualization System for Evolutionary Neural Networks for Deep Learning. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 4498-4502. 9006470. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). doi: 10.1109/BigData47090.2019.9006470

Visualization System for Evolutionary Neural Networks for Deep Learning

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