Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization

Usmani, Usman Ahmad and Usmani, Mohammed Umar (2023) Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization. In: 2023 World Conference on Communication & Computing (WCONF) , July 14-16, 2023 , Raipur, India. pp. 1-8.. ISBN 979-8-3503-2276-7

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DOI/Official URL: https://doi.org/10.1109/WCONF58270.2023.10235042

Abstract

This work aims to provide profound insights into neural networks and deep learning, focusing on their functioning, interpretability, and generalization capabilities. It explores fundamental aspects such as network architectures, activation functions, and learning algorithms, analyzing their theoretical foundations. The paper delves into the theoretical analysis of deep learning models, investigating their representational capacity, expressiveness, and convergence properties. It addresses the crucial issue of interpretability, presenting theoretical approaches for understanding the inner workings of these models. Theoretical aspects of generalization are also explored, including overfitting, regularization techniques, and generalization bounds. By advancing theoretical understanding, this paper paves the way for informed model design, improved interpretability, and enhanced generalization in neural networks and deep learning, pushing the boundaries of their application in diverse domains.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Neural networks, Deep learning, Interpretability, Generalization, Theoretical analysis
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Faculty of Civil Engineering Technology
Depositing User:	Miss Amelia Binti Hasan
Date Deposited:	19 Oct 2023 03:49
Last Modified:	19 Oct 2023 03:52
URI:	http://umpir.ump.edu.my/id/eprint/38934
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