International Conference on Deep Learning Theory and Applications(ICDLTA 2020 )

July 8-10,2020, Lieusaint-Paris,France

Call for Papers

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.

Topics of Interest :

• Models And Algorithms
• Recurrent Neural Network (RNN),Sparse Coding,Neuro-Fuzzy Algorithms
• Evolutionary Methods,Convolutional Neural Networks (CNN),Deep Hierarchical Networks (DHN),Dimensionality Reduction
• Machine Learning,Active Learning,Meta-Learning and Deep Networks
• Deep Metric Learning Methods,MAP Inference in Deep Networks
• Deep Reinforcement Learning,Learning Deep Generative Models
• Big Data Analytics,Extracting Complex Patterns,IoT and Smart Devices,Security Threat Detection
• Computer Vision Applications,Image Classification
• Object Detection,Face Recognition
• Natural Language Understanding
• Sentiment Analysis,Mobile Text Messaging Applications
• Content Filtering on Social Networks,Recommender Systems

Paper Submission

Authors should submit a paper in English, carefully checked for correct grammar and spelling, addressing one or several of the conference areas or topics. Each paper should clearly indicate the nature of its technical/scientific contribution, and the problems, domains or environments to which it is applicable. To facilitate the double-blind paper evaluation method, authors are kindly requested to produce and provide the paper WITHOUT any reference to any of the authors, including the authors’ personal details, the acknowledgments section of the paper and any other reference that may disclose the authors’ identity.

Important Dates