Deep Learning Experiments and Claims
Read Original本文深入讨论了在深度学习研究论文中，如何通过严谨的实验设计来支持或强化所提出的主张（claims）。文章以BatchNorm等经典论文为例，分析了仅证明结果有效与过度声称原理之间的常见问题，并介绍了从系统级实验、消融实验到深入分析的不同实验层次。作者强调，应根据实验证据的强度来恰当地表述主张，避免将直觉或推测当作事实陈述，这对于提高研究的严谨性和可信度至关重要。
Deep Learning Experiments and Claims
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