AI 与机器学习2019arxiv.org
机器学习学术研究中令人不安的趋势
Troubling trends in machine learning scholarship (2018)
入门 · 约 45 分钟读完前置知识：机器学习基础、学术论文阅读经验
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这篇论文指出机器学习学术研究中四种令人担忧的趋势：混淆解释与猜测、未能识别性能提升的真正来源（如把调参的功劳归于架构改进）、用数学来迷惑或炫技而非澄清、以及滥用术语。作者坦诚自己也犯过这些错误，呼吁学界提高严谨性。
HN 关注点：学术诚信和研究质量问题，讨论焦点是这些不良风气是否普遍存在于 ML 乃至整个学术界，以及其根源。
后来怎么样
这篇论文在机器学习社区引发了对学术严谨性的广泛讨论，'mathiness'等概念被后续方法论批评工作频繁引用，成为审视 ML 论文写作规范的常用参照。
HN 评论精选
这几条评论里：支持 1 补充 1 亲历 1
亲历以亲历者身份指出这种风气遍布各技术领域，并认为根源是学术晋升机制筛选出了擅长包装而非创新的教授
这不只是机器学习的问题，大多数技术领域都如此。我以前做显微图像分析，论文常用花哨的数学和时髦名词掩盖其实没有新东西的事实。最离谱的例子是一篇声称用压缩感知做超分辨显微的高影响力论文，去掉包装后其实就是反卷积，效果也一样，却发在 Nature Methods 上被引 360 次——因为没人真正懂压缩感知。我质问一位教授时，他说没时间先去读压缩感知文献再评这篇论文。根源在于：如今的教授大多不是擅长创新，而是擅长钻营；领域不成熟时还能发好论文，领域饱和后这些钻营者就只会用花哨数学包装无意义的进展塞进论文。
ramraj07查看英文原文
This is not just isolated to machine learning, but most technical fields (at least one other to my knowledge). I used to work in microscopy image analysis and the papers often would obfuscate the fact that they were not exactly doing anything new by using what looks like fancy math and some trendy names. One of the most outrageous examples is this "high profile" paper that says it does compressive sensing with superresolution microscopy - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3477591/ except I don't think they do; the math when you remove the bullshit sounds more like deconvolution than anything else (and the results are only as good). Yet, it got reviewed and accepted by Nature Methods, and is cited by 360 papers already. Why? Apparently no one in this field knows what compressive sensing really means. At least one professor in the field when I confronted him, just said he doesn't have time to go through compressive sensing literature first before evaluating this paper. What's the root cause? Frankly I'd argue the majority of professors nowadays aren't smart in innovation but smart in hustling. Because hustlers are who become professors in today's academic climate. They are able to publish good papers still if the field isn't mature, but if the field is saturated, you have to be really smart to make meaningful progress, and these hustlers are not. So they just try to find some way to wrap meaningless progress in fancy math and shove it in papers. The papers also go to reviewers who are similar hustlers (not every paper can be reviewed by Hinton ) so they either don't notice the problem or they do but let it slide because it's just their colleague (yay for journals asking for "suggested reviewers" to the authors itself).
支持引用论文核心内容，并指出作者自我批评的可贵之处，认为问题部分源于神经网络本身难以精确描述
论文聚焦四种趋势：(i) 不区分解释与猜测；(ii) 不指明经验性提升的来源，如把实际来自超参调优的收益归功于不必要的网络架构修改；(iii) mathiness：用数学来迷惑或炫技而非澄清；(iv) 误用语言，如选用带口语含义的术语或给既有技术术语赋予新义。值得肯定的是，作者承认自己也在多篇论文中犯过这些错。或许可以这样理解：神经网络已成为计算上的庞然大物，要精确地谈论它们变得非常困难，语言晦涩而含混。
joe_the_user查看英文原文
"In this paper, we focus on the following four patterns that appear to us to be trending in ML scholarship: (i) failure to distinguish between explanation and speculation; (ii) failure to identify the sources of empirical gains, e.g., emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning; (iii) mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g., by confusing technical and non-technical concepts; and (iv) misuse of language, e.g., by choosing terms of art with colloquial connotations or by overloading established technical terms. " To their credit, the authors actually own-up to doing this themselves in various papers. It seems like a way to describe the situation is that neural nets have become such computational monsters that talking about them exactly becomes very difficult with the language opaque and ambiguous.
补充补充指向该论文此前的 HN 讨论帖
之前的讨论在这里：https://news.ycombinator.com/item?id=17497273
cosmic_ape查看英文原文
previous discussion here: https://news.ycombinator.com/item?id=17497273
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