The New Special Issue "Information Theory in Emerging Machine Learning Techniques" is Open for Submission!

Guest Editor: Dr. Ke Sun

Submit to the Special Issue: https://www.mdpi.com/journal/entropy/special_issues/R4FG64B9J5

Submission deadline: 15 March 2025

Special Issue Information: In the past two decades, deep learning, as part of machine learning, has undergone significant development. Many emerging techniques have achieved state-of-the-art performance across diverse learning tasks and areas of application, such as natural language processing, robotics, multimedia processing, and healthcare. However, many of these new methods are based on empirical evidence. While theoretical machine learning and its relationships with information theory are well developed, the theoretical analysis for deep learning has not kept pace with the engineering advancements of new learning mechanisms.

There are substantial aspects of deep learning that are not common in other areas, like its unique properties of generalization, representation learning, and latent features, its interaction with optimization, generalization and over-parameterization, layer-wise aspects of the representation, stability, and robustness. These provide a rich foundation for the application and use of information theory.

Information theory has been fundamental to modern machine learning and can significantly contribute to the development of deep learning theory. This Special Issue aims to (1) provide information-theoretical insights into new deep learning methods and (2) develop new deep learning mechanisms, or adapt current mechanisms grounded in information theory. Its focus on emerging machine learning techniques indicates a particular interest in cutting-edge deep learning techniques that have not been analyzed previously and have not been examined through simplified architectures.