论文代码是否应该公开已是争论已久的问题，有从业者呼吁通过代码提交减少当下各类论文中的「水分」，也有研发人员表示「代码提交」类问题得因「研究」而异。不过单从我们最关心的各类顶会而言，似乎可以简单从三个角度去看待此问题：如果评审能够拿到底层代码或者数据，是否能更好地做评审工作？开放代码对作者有好处吗？对读者有好处吗？
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算法：研究目标是发现一些更好的算法以解决各种学习问题，是顶会中最典型的类型。
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理论：研究目标是一般性地理解哪些学习算法是可能的，哪些是不可能的。虽然这些论文同样可能提出算法，但它们通常并不要求一定要实现，因为这会浪费作者、评审者和读者的时间。
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应用：这些研究的目标是解决特定的任务。AlphaGoZero 就是一个例子，它在围棋领域中用算法击败了世界冠军。对于这类研究而言，由于计算量大、数据所有权等特点，编程的可复现性可能不切实际。
参考链接：https://www.reddit.com/r/MachineLearning/comments/fzss9t/d_if_a_paper_or_project_doesnt_publicly_release/
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