Advances in Deep Learning III

Dear Colleagues,

Deep learning is one of the research topics that attract a lot of attention from researchers in academia and industry. Compared to traditional machine learning methods, deep learning algorithms demonstrate their ability to train models from large-volume data sets. Also, those algorithms have significantly surpassed the performance of traditional methodologies for computer vision, natural language processing, robotics, and other fields. In recent years, a variety of theories and algorithms have advanced significantly in the field of artificial intelligence, including neural network structure, optimization, data representation, and deep reinforcement learning. Deep learning models such as attention mechanisms, and hostile generative networks, and GPT-3 (Generative Pre-trained Transformer 3) have also been developed and have achieved incredible achievements and successes. The proposal for this Special Issue is about the latest theoretical and practical applications of deep learning. In this regard, models derived from previous studies can be improved through research on the application of deep learning techniques to data analysis in different domains of research or industry.

The purpose of our Special Issue is to contribute to the demonstration of innovative algorithms and application areas of deep learning to solve problems in various research domains. Eventually, we aim to promote research and development of deep learning, by publishing high-quality original research articles, reviews, theoretical and critical perspectives, and viewpoint articles on the following topics:

• Deep Learning Algorithm/Architectures/Theory
• Deep Reinforcement Learning
• Adversarial Examples
• Deep Generative Models
• Multitask, Transfer, and Meta Learning
• Bayesian Methods
• Explainable and Interpretable AI
• Representation Learning
• Constrained Optimization
• Computer Vision
• Natural Language Processing (NLP)
• Sentiment Analysis

Prof. Dr. Wonjoon Kim
Prof. Dr. Sekyoung Youm
Prof. Dr. Sungbum Jun
Guest Editors

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