nips论文格式

nips论文格式

发布论文可以通过以下两种方法：

1、通过导师介绍；

2、自主选择论文代发机构发表。

选择期刊

准备提交在提交稿件之前，您必须选择要发布的期刊。重要的是要注意，每种期刊都有自己的风格和特征，并且通过选择与论文领域相匹配的期刊来更容易出版。有数百种期刊，每种期刊都有自己的要求。

修改稿件

论文通过后，期刊编辑将与作者联系。一般来说，论文不会一次通过。需要对其进行多次修改以编辑手稿并提交评论和要求。此时，仅需要根据编辑者的要求进行修改。

与负责编辑交谈时，应保持真诚开放的态度，这将有助于拉近两个人之间的距离，为您提供更好的建议，并且出版更加顺畅。

1）题目

题目是眼睛，简单切题即可。别太啰嗦，当然也要限定清楚，比如remote estimation of chlorophyll using MODIS images，如果你的内容只是针对一种材料，如小麦，最好题目里面反映出来，因为这种方法可能对其他作物并不适用。改为remote estimation of chlorophyll in wheat using MODIS images要好一些。

2）引言

引言是审稿人评价你对本领域认识的部分，这部分主要是叙述该领域的发展状况。通常被忽略的是引言的逻辑。首先要对研究现状的概述，从概述中引出存在的问题，然后是引出自己的文章，要做的东西，关键是把自己的东西怎么引出来，这是引言最重要的。之前的文章被加拿大的一个老外修改，最差的就是引言，没有逻辑性。

3）方法

这部分是数据的基础，一定要写清楚，比如用什么仪器，什么型号，什么材料，哪里产的。遥感里面的实验一般是光谱仪，参考版，作物名称，土壤特性，观测时间，等等。记得小麦的试验中，写光谱仪的高度，距离冠层还是地面，都要写得很清楚。不要怕麻烦，一定要把怎么写的讲清楚，个人觉得如果说不清楚的就不要提，避免越描越黑。

4）结论和分析

这部分是主体，结论里面不要有太多叙述性的东西，很多人结论里面还有别人的文献，这个需要避免，直接把你的结果说清楚即可。分析主要包括精度比较高低，优劣，原因分析，辅助数据，和他人文章结果的间接印证。间接验证是反映你的结果最好的方法。

如何写学术论文的rebuttal

1. Rebuttal的基本格式
一般rebuttal都有比较严格的篇幅要求，比如不能多于500或600个词。所以rebuttal的关键是要在有限的篇幅内尽可能清晰全面的回应数个reviewer的关注问题，做到释义清楚且废话少说。目前我的rebuttal的格式一般如下所示：

<img src="https://www.lw881.com/uploadfile/202303/db32891bed86a05.png" data-rawwidth="1592" data-rawheight="1124" class="origin_image zh-lightbox-thumb" width="1592" data-original="https://pic1.zhimg.com/v2-d3e09fcfcd5ca745557fa83ad26bf580_r.png">
其中，不同reviewer提出的同样的问题可以不用重复回答，可以直接"Please refer to A2 to reviewer#1"。结构清晰的rebuttal能够对reviewer和area chair提供极大的便利，也便于理解。
2. Rebuttal的内容
Rebuttal一定要着重关注reviewer提出的重点问题，这些才是决定reviewer的态度的关键，不要尝试去回避这种问题。回答这些问题的时候要直接且不卑不亢，保持尊敬的同时也要敢于指出reviewer理解上的问题。根据我的审稿经验，那些明显在回避一些问题的response只会印证自己的负面想法；而能够直面reviewer问题，有理有据指出reviewer理解上的偏差的response则会起到正面的效果。（PS: 如果自己的工作确实存在reviewer提出的一些问题，不妨表示一下赞同，并把针对这个问题的改进列为future work)

面对由于reviewer理解偏差造成全部reject的情况，言辞激烈一点才有可能引起Area Chair的注意，有最后一丝机会，当然，最基本的礼貌还是要有，不过很有可能有负面的效果，参考今年ICLR LipNet论文rebuttal 。

3. Rebuttal的意义
大家都知道通过rebuttal使reviewer改分的概率很低，但我认为rebuttal是一个尽人事的过程，身边也确实有一些从reject或borderline通过rebuttal最终被录用的例子。尤其像AAAI/IJCAI这种AI大领域的会议，最近两年投稿动则三四千篇，这么多reviewer恰好是自己小领域同行的概率很低，难免会对工作造成一些理解上的偏差甚至错误，此时的rebuttal就显得特别重要。所以对于处于borderline或者由于错误理解造成低分的论文，一定!一定!一定!要写好rebuttal!
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最后贴一下LeCun在CVPR2012发给pc的一封withdrawal rebuttal镇楼（该rebuttal被pc做了匿名处理），据说促成了ICLR的诞生，希望自己以后也有写这种rebuttal的底气：）
Hi Serge,

We decided to withdraw our paper #[ID no.] from CVPR "[Paper Title]" by [Author Name] et al.
We posted it on ArXiv: [Paper ID] .

We are withdrawing it for three reasons: 1) the scores are so low, and the reviews so ridiculous, that I don't know how to begin writing a rebuttal without insulting the reviewers; 2) we prefer to submit the paper to ICML where it might be better received; 3) with all the fuss I made, leaving the paper in would have looked like I might have tried to bully the program committee into giving it special treatment.

Getting papers about feature learning accepted at vision conference has always been a struggle, and I've had more than my share of bad reviews over the years. Thankfully, quite a few of my papers were rescued by area chairs.

This time though, the reviewers were particularly clueless, or negatively biased, or both. I was very sure that this paper was going to get good reviews because: 1) it has two simple and generally applicable ideas for segmentation ("purity tree" and "optimal cover"); 2) it uses no hand-crafted features (it's all learned all the way through. Incredibly, this was seen as a negative point by the reviewers!); 3) it beats all published results on 3 standard datasets for scene parsing; 4) it's an order of magnitude faster than the competing methods.

If that is not enough to get good reviews, I just don't know what is.

So, I'm giving up on submitting to computer vision conferences altogether. CV reviewers are just too likely to be clueless or hostile towards our brand of methods. Submitting our papers is just a waste of everyone's time (and incredibly demoralizing to my lab members)

I might come back in a few years, if at least two things change:
- Enough people in CV become interested in feature learning that the probability of getting a non-clueless and non-hostile reviewer is more than 50% (hopefully [Computer Vision Researcher]'s tutorial on the topic at CVPR will have some positive effect).
- CV conference proceedings become open access.

We intent to resubmit the paper to ICML, where we hope that it will fall in the hands of more informed and less negatively biased reviewers (not that ML reviewers are generally more informed or less biased, but they are just more informed about our kind of stuff). Regardless, I actually have a keynote talk at [Machine Learning Conference], where I'll be talking about the results in this paper.

Be assured that I am not blaming any of this on you as the CVPR program chair. I know you are doing your best within the traditional framework of CVPR.

I may also submit again to CV conferences if the reviewing process is fundamentally reformed so that papers are published before they get reviewed.

You are welcome to forward this message to whoever you want.

I hope to see you at NIPS or ICML.

Cheers,

-- [Author]