On Training Deep Neural Networks Using a Streaming Approach

Piotr Duda, Maciej Jaworski, Andrzej Cader, Lipo Wang
<span title="2020-01-01">2020</span> <i title="Walter de Gruyter GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r6s7pm3e6nafzezoucv6lewdm4" style="color: black;">Journal of Artificial Intelligence and Soft Computing Research</a> </i> &nbsp;
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
more &raquo; ... d 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2478/jaiscr-2020-0002">doi:10.2478/jaiscr-2020-0002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dfnv4veshbhx3i42btboou5bfa">fatcat:dfnv4veshbhx3i42btboou5bfa</a> </span>
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