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Title: A layer-wise theoretical framework for deep learning of convolutional neural networks
Authors: Nguyen, Huu-Thiet
Li, Sitan
Cheah, Chien Chern
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Nguyen, H., Li, S. & Cheah, C. C. (2022). A layer-wise theoretical framework for deep learning of convolutional neural networks. IEEE Access, 10, 14270-14287.
Project: 1922200001
Journal: IEEE Access
Abstract: As research attention in deep learning has been focusing on pushing empirical results to a higher peak, remarkable progress has been made in the performance race of machine learning applications in the past years. Yet deep learning based on artificial neural networks still remains difficult to understand as it is considered as a black-box approach. A lack of understanding of deep learning networks from the theoretical perspective would not only hinder the employment of them in applications where high-stakes decisions need to be made, but also limit their future development where artificial intelligence is expected to be robust, predictable and trustable. This paper aims to provide a theoretical methodology to investigate and train deep convolutional neural networks so as to ensure convergence. A mathematical model based on matrix representations for convolutional neural networks is first formulated and an analytic layer-wise learning framework for convolutional neural networks is then proposed and tested on several common benchmarking image datasets. The case studies show a reasonable trade-off between accuracy and analytic learning, and also highlight the potential of employing the proposed layer-wise learning method in finding the appropriate number of layers in actual implementations.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2022.3147869
Rights: © 2022 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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