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MobDL: A framework for profiling deep learning models: A case study using mobile digital health applications

conference contribution
posted on 2020-12-07, 00:00 authored by A R M Forkan, P P Jayaraman, R Kaul, Yuxin ZhangYuxin Zhang, C Mccarthy, P Delir Haghighi, R Ranjan
MobDL: A framework for profiling deep learning models: A case study using mobile digital health applications

History

Event

EAI Mobile and Ubiquitous Systems : Computing, Networking and Services. Conference (17th : 2020 : Darmstadt, Germany)

Pagination

405 - 414

Publisher

Association for Computing Machinery

Location

Darmstadt, Germany

Place of publication

New York, N.Y.

Publisher DOI

Start date

2020-12-07

End date

2020-12-09

ISBN-13

9781450388405

Language

eng

Author URL

Publication classification

E1 Full written paper - refereed

Title of proceedings

MobiQuitous 2020 : Proceedings of the 17th EAI International Conference on Mobile and Ubiquitous Systems : Computing, Networking and Services

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MobDL: A framework for profiling deep learning models: A case study using mobile digital health applications
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Categories

Keywords

CLASSIFICATIONCNNComputer ScienceComputer Science, Information SystemsComputer Science, Interdisciplinary ApplicationsComputer Science, Theory & MethodsDeep LearningDigital HealthFrameworkProfilingScience & TechnologyTechnologyTelecommunications

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