Tensorlab+：张量研究可重复性案例研究

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computing in Science & Engineering Pub Date : 2023-12-11 DOI:10.1109/mcse.2023.3340434

Stijn Hendrikx, Raphaël Widdershoven, Nico Vervliet, Lieven De Lathauwer

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引用次数: 0

摘要

张量方法是一类重要的基本技术，它将矩阵方法推广到多向数据和模型中。我们最近发布了 Tensorlab+，这是一个可下载的代码和数据档案库，允许同行重现我们在张量分解和应用方面的出版物中报道的实验。我们将简要讨论基本的张量工具，并介绍 Tensorlab+ 的内容。我们将详细介绍为确保实验的可重复性和代码质量所采取的步骤。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Tensorlab+: A Case Study on Reproducibility in Tensor Research

Tensor methods emerge as an important class of basic techniques, generalizing matrix methods to multiway data and models. We have recently released Tensorlab+, which is a downloadable archive of code and data that allows peers to reproduce the experiments reported in our publications on tensor decompositions and applications. We briefly discuss the basic tensor tools and give an introduction to the contents of Tensorlab+. We elaborate on the steps that were taken to ensure the reproducibility of the experiments and the quality of the code.

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来源期刊

Computing in Science & Engineering 工程技术-计算机：跨学科应用

CiteScore

4.20

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Physics, medicine, astronomy -- these and other hard sciences share a common need for efficient algorithms, system software, and computer architecture to address large computational problems. And yet, useful advances in computational techniques that could benefit many researchers are rarely shared. To meet that need, Computing in Science & Engineering presents scientific and computational contributions in a clear and accessible format. The computational and data-centric problems faced by scientists and engineers transcend disciplines. There is a need to share knowledge of algorithms, software, and architectures, and to transmit lessons-learned to a broad scientific audience. CiSE is a cross-disciplinary, international publication that meets this need by presenting contributions of high interest and educational value from a variety of fields, including—but not limited to—physics, biology, chemistry, and astronomy. CiSE emphasizes innovative applications in advanced computing, simulation, and analytics, among other cutting-edge techniques. CiSE publishes peer-reviewed research articles, and also runs departments spanning news and analyses, topical reviews, tutorials, case studies, and more.