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Using Syntactic Similarity to Shorten the Training Time of Deep Learning Models Using Time Series Datasets: A Case Study

AuthID
P-00V-CPT

Author(s)

Malta, S

Pinto, P

Veiga, MF

Editor(s)

Fred,A;Sansone,C;Madani,K

Document Type

Proceedings Paper

Year published

2021

Published

in PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON DEEP LEARNING THEORY AND APPLICATIONS (DELTA)

Pages: 93-100 (8)

Conference

2Nd International Conference on Deep Learning Theory and Applications (Delta), Date: JUL 07-09, 2021, Location: ELECTR NETWORK, Sponsors: INSTICC

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Wos^®

Scopus^®

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Google Scholar^®

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Publication Identifiers

DOI: 10.5220/0010515700930100

Scopus: 2-s2.0-85111875331

Wos: WOS:000821081200009

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Using Syntactic Similarity to Shorten the Training Time of Deep Learning Models Using Time Series Datasets: A Case Study

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Using Syntactic Similarity to Shorten the Training Time of Deep Learning Models Using Time Series Datasets: A Case Study

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