选自 | derekchia.com 作者 | Zhiyuan Li 等
编译 | 机器之心 编辑 | 小舟、魔王
论文复现一直是机器学习领域的难题，虽然最近多个学术会议提倡大家提交代码，但解决论文复现问题仍然任重而道远。在试图复现机器学习论文时，我们经常遇到哪些问题呢？新加坡机器学习工程师 Derek Chia 对此进行了总结。
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一些达到新 SOTA 的论文在新闻媒体中引起关注；
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读者深入研究或快速浏览论文内容；
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读者对论文中的实验结果印象深刻，并产生复现的兴趣。
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README 文件不完整或缺失；
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未定义依赖项、代码存在 bug、缺少预训练模型；
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未公开参数；
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私有数据集或缺少预处理步骤；
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对 GPU 资源的需求不切实际。
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Why Can’t I Reproduce Their Results (http://theorangeduck.com/page/reproduce-their-results)
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Rules of Machine Learning: Best Practices for ML Engineering (https://developers.google.com/machine-learning/guides/rules-of-ml)
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Curated list of awesome READMEs (https://github.com/matiassingers/awesome-readme)
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How the AI community can get serious about reproducibility (https://ai.facebook.com/blog/how-the-ai-community-can-get-serious-about-reproducibility/)
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ML Code Completeness Checklist (https://medium.com/paperswithcode/ml-code-completeness-checklist-e9127b168501)
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Designing the Reproducibility Program for NeurIPS 2020 (https://medium.com/@NeurIPSConf/designing-the-reproducibility-program-for-neurips-2020-7fcccaa5c6ad)
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Tips for Publishing Research Code (https://github.com/paperswithcode/releasing-research-code)
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本篇文章来源于: 深度学习这件小事
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