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

8 Citations (Scopus)


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 2018 IEEE 17th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018
Publication statusPublished - Jul 2018
Externally publishedYes


  • 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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