Please use this identifier to cite or link to this item:
Title: On training deep neural networks using a streaming approach
Authors: Duda, Piotr
Jaworski, Maciej
Cader, Andrzej
Wang, Lipo
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Duda, P., Jaworski, M., Cader, A., & Wang, L. (2019). On training deep neural networks using a streaming approach. Journal of Artificial Intelligence and Soft Computing Research, 10(1), 15-26. doi:10.2478/jaiscr-2020-0002
Journal: Journal of Artificial Intelligence and Soft Computing Research
Abstract: In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness results from an ability to analyze large labeled datasets. The price for such high accuracy is the long training time, necessary to process such large amounts of data. On the other hand, along with the increase in the number of collected data, the field of data stream analysis was developed. It enables to process data immediately, with no need to store them. In this work, we decided to take advantage of the benefits of data streaming in order to accelerate the training of deep neural networks. The work includes an analysis of two approaches to network learning, presented on the background of traditional stochastic and batch-based methods.
ISSN: 2083-2567
DOI: 10.2478/jaiscr-2020-0002
Schools: School of Electrical and Electronic Engineering 
Rights: © 2019 The Author(s) (published by Sciendo). This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Citations 20

Updated on Jun 9, 2024

Web of ScienceTM
Citations 20

Updated on Oct 28, 2023

Page view(s)

Updated on Jun 15, 2024

Download(s) 50

Updated on Jun 15, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.