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DeLTA 2020: International Conference on Deep Learning Theory and Applications

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嵌入截止倒计时徽章
DeLTA
截稿日期:
2020-03-06 Extended
通知日期:
2020-04-15
会议日期:
2020-07-08
会议地点:
Paris, France
届数:
1
浏览: 10976   关注: 1   参加: 0

征稿

DeLTA 2020 (International Conference on Deep Learning Theory and Applications) is an academic conference held in Paris, France on 2020-07-08. The paper submission deadline is 2020-03-06 (extended). Acceptance notifications are sent on 2020-04-15.

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 AREAS Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas: 1. MODELS AND ALGORITHMS 2. MACHINE LEARNING 3. BIG DATA ANALYTICS 4. COMPUTER VISION APPLICATIONS 5. NATURAL LANGUAGE UNDERSTANDING AREA 1: MODELS AND ALGORITHMS Recurrent Neural Network (RNN) Sparse Coding Neuro-Fuzzy Algorithms Evolutionary Methods Convolutional Neural Networks (CNN) Deep Hierarchical Networks (DHN) Dimensionality Reduction Unsupervised Feature Learning Deep Boltzmann Machines Generative Adversarial Networks (GAN) Autoencoders Deep Belief Networks AREA 2: 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 Deep Kernel Learning Graph Representation Learning Gaussian Processes for Machine Learning Clustering, Classification and Regression Classification Explainability AREA 3: BIG DATA ANALYTICS Extracting Complex Patterns IoT and Smart Devices Security Threat Detection Semantic Indexing Data Tagging Fast Information Retrieval Scalability of Models Data Integration and Fusion High-Dimensional Data Streaming Data Genomics and Bioinformatics AREA 4: COMPUTER VISION APPLICATIONS Image Classification Object Detection Face Recognition Facial Expression Analysis Action Recognition Human Pose Estimation Image Retrieval Semantic Segmentation Deep Image Denoising AREA 5: NATURAL LANGUAGE UNDERSTANDING Sentiment Analysis Mobile Text Messaging Applications Question Answering Applications Speech Interfaces Language Translation Document Summarization Content Filtering on Social Networks Recommender Systems
由 Dou Sun 最后更新于

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