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 ProceedingsConference paper (refereed)Researchpeer-review

9 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 languageEnglish
Title of host publicationProceedings of the 17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 18
EditorsYingxu WANG, Sam KWONG, Jerome FELDMAN , Newton HOWARD, Phillip SHEU, Bernard WIDROW
PublisherIEEE
Pages6-15
Number of pages10
ISBN (Electronic)9781538633601
ISBN (Print)9781538633618
DOIs
Publication statusPublished - Jul 2018
Externally publishedYes
Event17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018 - Berkeley, United States
Duration: 16 Jul 201818 Jul 2018

Conference

Conference17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018
Country/TerritoryUnited States
CityBerkeley
Period16/07/1818/07/18

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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