Information-Theoretic Methods in Deep Learning: Theory and Applications - Tapa dura

 
9783725829828: Information-Theoretic Methods in Deep Learning: Theory and Applications
Tapa dura
ISBN 10: 3725829829 ISBN 13: 9783725829828
Editorial: Mdpi AG, 2025

Sinopsis

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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Editorial
Mdpi AG
Año de publicación
2025
Idioma
Inglés
ISBN 10 
3725829829
ISBN 13 
9783725829828
Encuadernación
Tapa dura
Número de páginas
244
Editor
Yang Shuangming, Shujian Yu Shujian, Giraldo Luis Gonzalo Sánchez
Contacto del fabricante
no disponible
Persona responsable
no disponible