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DeLTA 2024 : 5th International Conference on Deep Learning Theory and Applications


When Jul 10, 2024 - Jul 11, 2024
Where Dijon, France
Submission Deadline Apr 30, 2024
Notification Due Apr 15, 2024
Final Version Due Apr 30, 2024
Categories    machine learning   neural networks and artificial   e-learning, computers in educa   neural networks

Call For Papers


5th International Conference on Deep Learning Theory and Applications (DeLTA)
Submission Deadline: February 15, 2024

Endorsed by:
International Association for Pattern Recognition
In Cooperation with:
ACM Special Interest Group on Artificial Intelligence
Association for the Advancement of Artificial Intelligence
International Neural Network Society
European Society for Fuzzy Logic and Technology

Proceedings will be submitted for evaluation for indexation by:
Google Scholar
Japanese Science and Technology Agency (JST)
Norwegian Register for Scientific Journals and Series
Mathematical Reviews
Web of Science / Conference Proceedings Citation Index

The event will be of hybrid nature, in the sense that online presentations of accepted papers will be possible for those authors that are unable to travel to the venue.
July 10 - 11, 2024
Dijon, France

Deep Learning and Big Data Analytics are two major topics of data science, nowadays. Big Data has become important in practice, as many organizations have been collecting massive amounts of data that can contain useful information for business analysis and decisions, impacting existing and future technology. A key benefit of Deep Learning is the ability to process these data and extract high-level complex abstractions as data representations, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled.

Machine-learning and artificial intelligence are pervasive in most real-world applications scenarios such as computer vision, information retrieval and summarization from structured and unstructured multimodal data sources, natural language understanding and translation, and many other application domains. Deep learning approaches, leveraging on big data, are outperforming state-of-the-art more “classical” supervised and unsupervised approaches, directly learning relevant features and data representations without requiring explicit domain knowledge or human feature engineering. These approaches are currently highly important in IoT applications.

DeLTA is organized in 5 major tracks:
Machine Learning
Models and Algorithms
Big Data Analytics
Computer Vision Applications
Natural Language Understanding

Conference Chair(s)
Carlo Sansone, University of Naples Federico II, Italy
Oleg Gusikhin, Ford Motor Company, United States

Program Chair(s)
Ana Fred, Instituto de Telecomunicações and Instituto Superior Técnico (University of Lisbon), Portugal
Allel Hadjali, LIAS/ENSMA, Poitiers, France

Program Committee

DeLTA Secretariat

Address: Avenida de S. Francisco Xavier, Lote 7 Cv. C, Setubal 2900-616, Portugal
Tel:+351 265 520 185

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