Overview
Part of the book series: Communications in Computer and Information Science (CCIS, volume 2172)
Included in the following conference series:
Conference proceedings info: DeLTA 2024.
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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.
Keywords
- 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
Table of contents (24 papers)
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Back Matter
Other volumes
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Deep Learning Theory and Applications
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Deep Learning Theory and Applications
Editors and Affiliations
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: Ana Fred, Allel Hadjali, Oleg Gusikhin, Carlo Sansone
Series Title: Communications in Computer and Information Science
DOI: https://doi.org/10.1007/978-3-031-66705-3
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: 978-3-031-66704-6Published: 21 August 2024
eBook ISBN: 978-3-031-66705-3Published: 20 August 2024
Series ISSN: 1865-0929
Series E-ISSN: 1865-0937
Edition Number: 1
Number of Pages: XVII, 389
Number of Illustrations: 10 b/w illustrations, 115 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Computer Applications, Data Mining and Knowledge Discovery, Natural Language Processing (NLP)