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

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 theoreti...

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Main Authors: Usmani, Usman Ahmad, Usmani, Mohammed Umar
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/38934/1/Theoretical_Insights_into_Neural_Networks_and_Deep_Learning_Advancing_Understanding_Interpretability_and_Generalization.pdf
http://umpir.ump.edu.my/id/eprint/38934/2/Theoretical%20Insights%20into%20Neural%20Networks%20and%20Deep%20Learning.pdf
http://umpir.ump.edu.my/id/eprint/38934/
https://doi.org/10.1109/WCONF58270.2023.10235042
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spelling my.ump.umpir.389342023-10-19T03:52:57Z http://umpir.ump.edu.my/id/eprint/38934/ Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization Usmani, Usman Ahmad Usmani, Mohammed Umar TA Engineering (General). Civil engineering (General) 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. IEEE 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38934/1/Theoretical_Insights_into_Neural_Networks_and_Deep_Learning_Advancing_Understanding_Interpretability_and_Generalization.pdf pdf en http://umpir.ump.edu.my/id/eprint/38934/2/Theoretical%20Insights%20into%20Neural%20Networks%20and%20Deep%20Learning.pdf 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 https://doi.org/10.1109/WCONF58270.2023.10235042
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Usmani, Usman Ahmad
Usmani, Mohammed Umar
Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization
description 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.
format Conference or Workshop Item
author Usmani, Usman Ahmad
Usmani, Mohammed Umar
author_facet Usmani, Usman Ahmad
Usmani, Mohammed Umar
author_sort Usmani, Usman Ahmad
title Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization
title_short Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization
title_full Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization
title_fullStr Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization
title_full_unstemmed Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization
title_sort theoretical insights into neural networks and deep learning: advancing understanding, interpretability, and generalization
publisher IEEE
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/38934/1/Theoretical_Insights_into_Neural_Networks_and_Deep_Learning_Advancing_Understanding_Interpretability_and_Generalization.pdf
http://umpir.ump.edu.my/id/eprint/38934/2/Theoretical%20Insights%20into%20Neural%20Networks%20and%20Deep%20Learning.pdf
http://umpir.ump.edu.my/id/eprint/38934/
https://doi.org/10.1109/WCONF58270.2023.10235042
_version_ 1822923784426881024
score 13.232414

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