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Deep Learning Theory and Applications

5th International Conference, DeLTA 2024, Dijon, France, July 10–11, 2024, Proceedings, Part II

  • Conference proceedings
  • Aug 2024


Part of the book series: Communications in Computer and Information Science (CCIS, volume 2172)

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About this book

The two-volume set CCIS 2171 and 2172 constitutes the refereed papers from the 5th INternational Conference on Deep Learning Theory and Applications, DeLTA 2024, which took place in Dijon, France, during July 10-11, 2024. 

The 44 papers included in these proceedings were carefully reviewed and selected from a total of 70 submissions. They focus on topics such as deep learning and big data analytics; machine-learning and artificial intelligence, etc. 


  • deep hierarchical networks
  • evolutionary methods
  • generative adversarial networks
  • meta-learning and deep networks
  • models and algorithms
  • machine learning
  • deep metric learning methods
  • big-data analytics
  • deep reinforcement learning
  • computer vision applications
  • learning deep generative models
  • natural language understanding
  • graph representation learning
  • recurrent neural network
  • IoT and smart devices
  • smart indexing
  • convolutional neural networks
  • object detection
  • sentiment analysis
  • artificial intelligence

Editors and Affiliations

  • LIAS, LIAS/ENSMA, Poitiers, France

    Allel Hadjali

  • Ford Motor Company, Dearborn, USA

    Oleg Gusikhin

  • University of Naples Federico II, Naples, Italy

    Carlo Sansone

  • Instituto de Telecomunicações and University of Lisbon, Lisbon, Portugal

    Ana Fred

Bibliographic Information

  • Book Title: Deep Learning Theory and Applications

  • Book Subtitle: 5th International Conference, DeLTA 2024, Dijon, France, July 10–11, 2024, Proceedings, Part II

  • Editors: Allel Hadjali, Oleg Gusikhin, Carlo Sansone, Ana Fred

  • Series Title: Communications in Computer and Information Science

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

  • Softcover ISBN: 978-3-031-66704-6Due: 08 September 2024

  • eBook ISBN: 978-3-031-66705-3Due: 08 September 2024

  • Series ISSN: 1865-0929

  • Series E-ISSN: 1865-0937

  • Edition Number: 1

  • Number of Pages: XII, 382

  • Number of Illustrations: 121 b/w illustrations

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