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...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38934/ 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 |
| _version_ | 1848825636897947648 |
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| author | Usmani, Usman Ahmad Usmani, Mohammed Umar |
| author_facet | Usmani, Usman Ahmad Usmani, Mohammed Umar |
| author_sort | Usmani, Usman Ahmad |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:32:05Z |
| format | Conference or Workshop Item |
| id | ump-38934 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:32:05Z |
| publishDate | 2023 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 (Published) https://doi.org/10.1109/WCONF58270.2023.10235042 |
| 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 |
| title | 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_short | 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 |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/38934/ http://umpir.ump.edu.my/id/eprint/38934/ 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 |