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DeLTA 2023 : 4th International Conference on Deep Learning Theory and Applications | |||||||||||||||
Link: https://delta.scitevents.org | |||||||||||||||
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Call For Papers | |||||||||||||||
CALL FOR PAPERS
4th International Conference on Deep Learning Theory and Applications DeLTA website: https://delta.scitevents.org July 13 - 14, 2023 Rome, Italy IMPORTANT DATES: Regular Paper Submission: March 15, 2023 (extended) Authors Notification (regular papers): April 21, 2023 Final Regular Paper Submission and Registration: May 5, 2023 Position Paper Submission: April 7, 2023 Authors Notification (position papers): May 12, 2023 Final Regular Paper Submission and Registration: May 25, 2023 Scope: 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. Conference Topics: Area 1: Models and Algorithms - Recurrent Neural Network (RNN) - Evolutionary Methods - Convolutional Neural Networks (CNN) - Deep Hierarchical Networks (DHN) - Dimensionality Reduction - Unsupervised Feature Learning - Generative Adversarial Networks (GAN) - Autoencoders Area 2: Machine Learning - Active Learning - Meta-Learning and Deep Networks - Deep Metric Learning Methods - Deep Reinforcement Learning - Learning Deep Generative Models - Deep Kernel Learning - Graph Representation Learning - Clustering, Classification and Regression - Classification Explainability Area 3: Big Data Analytics - Extracting Complex Patterns - IoT and Smart Devices - Security Threat Detection - Semantic Indexing - Fast Information Retrieval - Scalability of Models - Data Integration and Fusion - High-Dimensional Data - Streaming Data Area 4: Computer Vision Applications - Image Classification - Object Detection - Face Recognition - Image Retrieval - Semantic Segmentation Area 5: Natural Language Understanding - Sentiment Analysis - Question Answering Applications - Language Translation - Content Filtering on Social Networks - Recommender Systems DeLTA KEYNOTE LECTURE Luís Paulo Reis, University of Porto, Portugal DeLTA CONFERENCE CO-CHAIRS: Oleg Gusikhin, Ford Motor Company, United States Carlo Sansone, University of Naples Federico II, Italy DeLTA PROGRAM CO-CHAIRS: Donatello Conte, Université de Tours, France Ana Fred, Instituto de Telecomunicações and University of Lisbon, Portugal PROGRAM COMMITTEE https://delta.scitevents.org/ProgramCommittee.aspx DeLTA Secretariat Address: Avenida de S. Francisco Xavier, Lote 7 Cv. C Tel: +351 265 520 185 Fax: +351 265 520 186 Web: https://delta.scitevents.org e-mail: delta.secretariat@insticc.org |
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