Information-Theoretic Methods in Deep Learning: Theory and Applications
Language: English
Published by Mdpi AG, 2025
- Hardcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
AbeBooks seller since March 25, 2015
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In English.
Seller Inventory # ria9783725829828_new
- Title
- Information-Theoretic Methods in Deep Learning: Theory and Applications
- Publisher
- Mdpi AG
- Publication year
- 2025
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3725829829
- ISBN 13
- 9783725829828
- Item weight
- 863 grams
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.
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Ria Christie Collections
Uxbridge, United Kingdom
AbeBooks seller since March 25, 2015
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