Information-Theoretic Methods in Deep Learning Theory and Applications

[voska89]

Free Download Information-Theoretic Methods in Deep Learning: Theory and Applications
by Shuangming Yang, Shujian Yu
English | 2025 | ISBN: 3725829829 | 246 Pages | True PDF | 17.3 MB

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.


Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live

Rapidgator
fqk88.7z.html
TakeFile
fqk88.7z.html
Fileaxa
fqk88.7z
Fikper
fqk88.7z.html


Links are Interchangeable - Single Extraction