A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks

Yingxu WANG; Henry LEUNG; Marina GAVRILOVA; Omar ZATARAIN; Daniel GRAVES; Jianhua LU; Newton HOWARD; Sam KWONG; Phillip SHEU; Shushma PATEL

doi:10.1109/ICCI-CC.2018.8482022

A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks

Yingxu WANG, Henry LEUNG, Marina GAVRILOVA, Omar ZATARAIN, Daniel GRAVES, Jianhua LU, Newton HOWARD, Sam KWONG, Phillip SHEU, Shushma PATEL

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

10 Citations (Scopus)

Abstract

Sequence learning is one of the hard challenges to current machine learning technologies and deep neural network technologies. This paper presents a literature survey and analysis on a variety of neural networks towards sequence learning. The conceptual models, methodologies, mathematical models and usages of classic neural networks and their learning capabilities are contrasted. Advantages and disadvantages of neural networks for sequence learning are formally analyzed. The state-of-the-art, theoretical problems and technical constraints of existing methodologies are reviewed. The needs for understanding temporal sequences by unsupervised or intensive-training-free learning theories and technologies are elaborated.

Original language	English
Title of host publication	Proceedings of the 17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 18
Editors	Yingxu WANG, Sam KWONG, Jerome FELDMAN , Newton HOWARD, Phillip SHEU, Bernard WIDROW
Publisher	IEEE
Pages	6-15
Number of pages	10
ISBN (Electronic)	9781538633601
ISBN (Print)	9781538633618
DOIs	https://doi.org/10.1109/ICCI-CC.2018.8482022
Publication status	Published - Jul 2018
Externally published	Yes
Event	17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018 - Berkeley, United States Duration: 16 Jul 2018 → 18 Jul 2018

Conference

Conference	17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018
Country/Territory	United States
City	Berkeley
Period	16/07/18 → 18/07/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Analytic methodologies
Applications
Cognitive systems
Deep NNs
Denotational mathematics
Language sequence learning
Neural networks (NNs)
Recurrent NNs
Sequence learning
Visual sequence learning

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10.1109/ICCI-CC.2018.8482022

Cite this

WANG, Y., LEUNG, H., GAVRILOVA, M., ZATARAIN, O., GRAVES, D., LU, J., HOWARD, N., KWONG, S., SHEU, P., & PATEL, S. (2018). A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks. In Y. WANG, S. KWONG, J. FELDMAN , N. HOWARD, P. SHEU, & B. WIDROW (Eds.), Proceedings of the 17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 18 (pp. 6-15). Article 8482022 IEEE. https://doi.org/10.1109/ICCI-CC.2018.8482022

WANG, Yingxu ; LEUNG, Henry ; GAVRILOVA, Marina et al. / A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks. Proceedings of the 17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 18 . editor / Yingxu WANG ; Sam KWONG ; Jerome FELDMAN ; Newton HOWARD ; Phillip SHEU ; Bernard WIDROW. IEEE, 2018. pp. 6-15

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abstract = "Sequence learning is one of the hard challenges to current machine learning technologies and deep neural network technologies. This paper presents a literature survey and analysis on a variety of neural networks towards sequence learning. The conceptual models, methodologies, mathematical models and usages of classic neural networks and their learning capabilities are contrasted. Advantages and disadvantages of neural networks for sequence learning are formally analyzed. The state-of-the-art, theoretical problems and technical constraints of existing methodologies are reviewed. The needs for understanding temporal sequences by unsupervised or intensive-training-free learning theories and technologies are elaborated.",

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author = "Yingxu WANG and Henry LEUNG and Marina GAVRILOVA and Omar ZATARAIN and Daniel GRAVES and Jianhua LU and Newton HOWARD and Sam KWONG and Phillip SHEU and Shushma PATEL",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018 ; Conference date: 16-07-2018 Through 18-07-2018",

year = "2018",

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doi = "10.1109/ICCI-CC.2018.8482022",

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WANG, Y, LEUNG, H, GAVRILOVA, M, ZATARAIN, O, GRAVES, D, LU, J, HOWARD, N, KWONG, S, SHEU, P & PATEL, S 2018, A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks. in Y WANG, S KWONG, J FELDMAN , N HOWARD, P SHEU & B WIDROW (eds), Proceedings of the 17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 18 ., 8482022, IEEE, pp. 6-15, 17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018, Berkeley, United States, 16/07/18. https://doi.org/10.1109/ICCI-CC.2018.8482022

A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks. / WANG, Yingxu; LEUNG, Henry; GAVRILOVA, Marina et al.
Proceedings of the 17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 18 . ed. / Yingxu WANG; Sam KWONG; Jerome FELDMAN ; Newton HOWARD; Phillip SHEU; Bernard WIDROW. IEEE, 2018. p. 6-15 8482022.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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WANG Y, LEUNG H, GAVRILOVA M, ZATARAIN O, GRAVES D, LU J et al. A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks. In WANG Y, KWONG S, FELDMAN J, HOWARD N, SHEU P, WIDROW B, editors, Proceedings of the 17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 18 . IEEE. 2018. p. 6-15. 8482022 doi: 10.1109/ICCI-CC.2018.8482022

A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks

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Keywords

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