Repository for Reusing Artifacts of Artificial Neural Networks

March 30, 2020 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .README.md.html, .gitignore, .mvn, FFMPEG, LICENSE, README.md, assets, bin, build.cmd, buildAndRun.cmd, buildWar.bat, buildWarWithoutTests.bat, clearDatabase.sh, conf, mvnw, mvnw.cmd, pom.xml, run.bat, src, test.cmd

Authors Javad Ghofrani, Ehsan Kozegar, Mohammad Divband Soorati, Arezoo Bozorgmehr, Hongfei Chen, Maximilian Naake arXiv ID 2003.13619 Category cs.LG: Machine Learning Cross-listed cs.SE Citations 0 Venue arXiv.org Repository https://github.com/ghofrani85/RAN2 ⭐ 1 Last Checked 2 months ago

Abstract

Artificial Neural Networks (ANNs) replaced conventional software systems in various domains such as machine translation, natural language processing, and image processing. So, why do we need an repository for artificial neural networks? Those systems are developed with labeled data and we have strong dependencies between the data that is used for training and testing our network. Another challenge is the data quality as well as reuse-ability. There we are trying to apply concepts from classic software engineering that is not limited to the model, while data and code haven't been dealt with mostly in other projects. The first question that comes to mind might be, why don't we use GitHub, a well known widely spread tool for reuse, for our issue. And the reason why is that GitHub, although very good in its class is not developed for machine learning appliances and focuses more on software reuse. In addition to that GitHub does not allow to execute the code directly on the platform which would be very convenient for collaborative work on one project.