Deep Learning-based Intelligent Systems: Theories, Algorithms, and Applications (SI-dlis)
• 大类 : 工程技术 - 3区
• 小类 : 计算机：硬件 - 3区
• 小类 : 计算机：跨学科应用 - 4区
• 小类 : 工程：电子与电气 - 4区
Deep learning has become a topic of increasing interest for researchers, from both academia and Industry, during the past decade. Unlike traditional machine learning methods, deep learning algorithms show the ability to learn and model very large-scale data sets. Deep learning techniques have achieved great success in different tasks in computer vision, natural language processing, robotics, and other areas. Recent years have witnessed a great development of the deep learning theory and various applications in the general field of artificial intelligence, including neural network structure, optimization, data representation, and deep reinforcement learning. Some extensions to the deep learning networks, e.g., attention mechanism, adversarial generative networks, and deep Q-network, were also developed, and remarkable achievements and successes have been achieved.
Although deep learning has been well studied in recent years, there exist many challenges to apply deep learning techniques in intelligent systems. First, deep learning approaches require a huge and diverse amount of data as input to models, and have a large number of parameters for training. Second, the training of deep models is easy to fall into over-fitting problems, and the transfer learning of deep models to other fields is also challenging. Besides, since deep learning models have transparency or black-box issues, it is hard to understand how a given system makes a decision, which, however, is important in some domains such as financial trading or medical diagnosis.
This special section solicits high-quality papers reporting on deep learning-based intelligent systems, with the goals of highlighting new achievements and developments as well as feature outstanding open issues and promising new directions on theories, algorithms, and applications. Particularly, the principal technical areas could be:
- Knowledge representation, storage, and processing