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

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, U.A., Usmani, M.U.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37565/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173043343&doi=10.1109%2fWCONF58270.2023.10235042&partnerID=40&md5=bdac334c46f1fe39a9595ff410135bf2
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Institution: Universiti Teknologi Petronas
id oai:scholars.utp.edu.my:37565
record_format eprints
spelling oai:scholars.utp.edu.my:375652023-10-13T12:52:56Z http://scholars.utp.edu.my/id/eprint/37565/ Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization Usmani, U.A. Usmani, M.U. 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. © 2023 IEEE. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item NonPeerReviewed Usmani, U.A. and Usmani, M.U. (2023) Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization. In: UNSPECIFIED. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173043343&doi=10.1109%2fWCONF58270.2023.10235042&partnerID=40&md5=bdac334c46f1fe39a9595ff410135bf2 10.1109/WCONF58270.2023.10235042 10.1109/WCONF58270.2023.10235042 10.1109/WCONF58270.2023.10235042
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
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. © 2023 IEEE.
format Conference or Workshop Item
author Usmani, U.A.
Usmani, M.U.
spellingShingle Usmani, U.A.
Usmani, M.U.
Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization
author_facet Usmani, U.A.
Usmani, M.U.
author_sort Usmani, U.A.
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 Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37565/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173043343&doi=10.1109%2fWCONF58270.2023.10235042&partnerID=40&md5=bdac334c46f1fe39a9595ff410135bf2
_version_ 1781707925388001280

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