Information-Theoretic Methods in Deep Learning: Theory and Applications - Hardcover

Hardcover

ISBN 10: 3725829829 ISBN 13: 9783725829828

Publisher: Mdpi AG, 2025

Synopsis

The rapid development of deep learning has led to groundbreaking advancements across various fields, from computer vision to natural language processing and beyond. Information theory, as a mathematical foundation for understanding data representation, learning, and communication, has emerged as a powerful tool in advancing deep learning methods. This Special Issue, "Information-Theoretic Methods in Deep Learning: Theory and Applications", presents cutting-edge research that bridges the gap between information theory and deep learning. It covers theoretical developments, innovative methodologies, and practical applications, offering new insights into the optimization, generalization, and interpretability of deep learning models. The collection includes contributions on: Theoretical frameworks combining information theory with deep learning architectures; Entropy-based and information bottleneck methods for model compression and generalization; Mutual information estimation for feature selection and representation learning; Applications of information-theoretic principles in natural language processing, computer vision, and neural network optimization.

Edition details

• Publisher: Mdpi AG
• Publication date: 2025
• Language: English
• ISBN 10: 3725829829
• ISBN 13: 9783725829828
• Binding: Hardcover
• Number of pages: 244
• Editor: Yang Shuangming, Shujian Yu Shujian, Giraldo Luis Gonzalo Sánchez