Deep Learning for Predicting Scientific Growth Trends

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Description

Poster for the 2018 International Conference on Knowledge Management. This poster proposes a supervised learning model based on Convolutional Neural Network (CNN).

Physical Description

1 poster : ill. ; 91 x 121 cm.

Creation Information

He, Jiangen November 9, 2018.

Context

This poster is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Arts and Sciences to Digital Library, a digital repository hosted by the UNT Libraries. More information about this poster can be viewed below.

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UNT College of Arts and Sciences

The UNT College of Arts and Sciences educates students in traditional liberal arts, performing arts, sciences, professional, and technical academic programs. In addition to its departments, the college includes academic centers, institutes, programs, and offices providing diverse courses of study.

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Description

Poster for the 2018 International Conference on Knowledge Management. This poster proposes a supervised learning model based on Convolutional Neural Network (CNN).

Physical Description

1 poster : ill. ; 91 x 121 cm.

Source

  • 14th International Conference on Knowledge Management, November 9-10, 2018. Vancouver, Canada.

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

Related Items

Deep Learning for Predicting Scientific Growth Trends (Paper)

Deep Learning for Predicting Scientific Growth Trends

This paper proposes a supervised learning model based on Convolutional Neural Network (CNN).

Relationship to this item: (Is Based On)

"Deep Learning for Predicting Scientific Growth Trends," ark:/67531/metadc1393779

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Creation Date

  • November 9, 2018

Added to The UNT Digital Library

  • Feb. 11, 2019, 9:44 a.m.

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He, Jiangen. Deep Learning for Predicting Scientific Growth Trends, poster, November 9, 2018; (digital.library.unt.edu/ark:/67531/metadc1451157/: accessed February 20, 2019), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.