The rewards of reusable machine learning code

IF 18.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Nature Machine Intelligence Pub Date : 2024-04-24 DOI:10.1038/s42256-024-00835-5

引用次数: 0

Abstract

Research papers can make a long-lasting impact when the code and software tools supporting the findings are made readily available and can be reused and built on. Our reusability reports explore and highlight examples of good code sharing practices.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

可重复使用的机器学习代码的回报

如果能随时提供支持研究成果的代码和软件工具，并能在此基础上重复使用，那么研究论文就能产生长久的影响。我们的可重用性报告探讨并强调了良好的代码共享实践范例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Nature Machine Intelligence Multiple-

CiteScore

36.90

自引率

2.10%

发文量

127

期刊介绍： Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.

期刊最新文献

Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep learning A soft skin with self-decoupled three-axis force-sensing taxels Efficient rare event sampling with unsupervised normalizing flows Clinical large language models with misplaced focus Efficient generation of protein pockets with PocketGen