Information Theory and Deep Neural Networks
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 15390
Special Issue Editors
Interests: computer vision; information theory; algebraic coding theory; machine learning; deep learning; internet; distributed storage
Special Issues, Collections and Topics in MDPI journals
Interests: information theory; data compression; algebraic coding theory; machine learning; deep learning; distributed storage
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Information theory answers two fundamental questions in communication theory, but its impact has expanded far beyond the field of communication. Many methods and ideas developed in information theory have been adopted to explain and uncover the internal mechanism in modern deep neural networks. For example, the information bottleneck has served as a tool in explaining the representation learning; mutual information has been adopted in deriving various types of generalization bounds in a deep neural network; quantization and other data compression techniques have been applied in the design of lightweight deep neural networks, namely model compression. Progress regarding an information theoretic understanding of deep neural networks has often been driven by the deep-learning-based application and induced phenomenon and is yet to be explored further.
Neural networks have a long history, aiming to understand how the human brain works and how what we call intelligence is formed. Neural networks with many layers, known as deep neural networks (DNNs), encompassing convolutional neural networks (CNN) and recurrent neural networks (RNN), have become popular and achieved state-of-the-art performance in various computer vision tasks. However, the use of deep neural networks to study and improve the classical source coding and channel coding problem in information theory is also yet to be explored. There have been some advances in applying DNNs to compressed sensing, image and video compression, channel decoding, and joint source-channel coding.
This Special Issue aims to provide an opportunity for the presentation of novel progress regarding the intersection between information theory and deep neural networks. Specifically, the information theoretic analysis and interpretation of DNN-based applications and induced phenomena, in addition to the design of improved coding schemes by DNNs in signal processing, data compression, channel coding or other topics in information theory, fall within the scope of this Special Issue. Contributions addressing any of these issues are very welcome.
Prof. Dr. Shu-Tao Xia
Dr. Bin Chen
Guest Editors
Manuscript Submission Information
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Keywords
- information theoretic techniques
- statistics
- deep learning
- model compression
- data compression
- compressed sensing
- optimization
- generalization
- generative adversarial networks
- graph convolutional networks
- joint source-channel coding
- applications
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