7 ms·
How to Read a Paper (2016) [pdf]
- hprotagonist 7y agoThere's a 2016 updated version here : https://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/07/paper-reading.pdf https://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/07/... Should the link update?
- analog31 7y agoMy spouse has a rule for science papers: First, look at the pictures. She figures that people will put a lot of effort into their graphs and diagrams telling a good story.
- haecceity 7y agoRats! I only use xkcd style plots.
- jimbokun 7y agoI think that's likely to get you a lot of readers.
- TheGallopedHigh 7y agoUsual method followed by a lot of academics is: read abstract first, then figures, and if it’s still appealing read the conclusion. If the paper is still appealing at that point read the whole thing. That way you can cycle through a lot and stay on top of the literature.
- el_cujo 7y agoThis is a good idea. Depending on the field, there really shouldn't be any statements in the text about the findings in the paper that aren't directly backed up by "proof" in a figure. If you're familiar enough with the methodology, just looking at the figures should give you a good idea of what the authors did.
- m3kw9 7y agoI tried to read this paper using his approach but I didn’t know where to start..
- ajkelly451 7y agoCame here to say the same thing, you beat me to it!
- m3kw9 7y agoThis is like a level 1 joke, there is an easy established pattern to it, kind of slap stick
- gchamonlive 7y agoThis reminds me of How to Read a Book[1], which is also a great read. [1] https://en.m.wikipedia.org/wiki/How_to_Read_a_Book https://en.m.wikipedia.org/wiki/How_to_Read_a_Book
- deleted 7y ago[deleted]
- TrackerFF 7y agoI know there are a lot of ML researchers and practitioners here - and I have unfortunately only a very shallow experience with reading ML papers, more so with the recent output. But, I have to ask, how do you get a feel that the content actually looks correct, and not just only quack? The improvements are usually in the 1% range, from old models to new models, and the models are complex. More often than not also lacking code, implementation / experiment procedures, etc. Basically, I have no idea if the paper is reproducible, if the results are cherry picked from hundreds / thousands of runs, if the paper is just cleverly disguised BS with pumped up numbers to get grants, and so on. As it is right now, I can only rely on expert assurance from those that peer review these papers - but even then, in the back of my mind, I'm wondering if they've had time to rigorously review a paper. The output of ML / AI papers these days is staggering, and the systems are so complex that I'd be impressed if some single post. doc or researcher would have time to reproduce results.
- ssivark 7y ago> The improvements are usually in the 1% range, from old models to new models, and the models are complex. More often than not also lacking code, implementation / experiment procedures, etc. [...] no idea if the paper is reproducible, if the results are cherry picked from hundreds / thousands of runs, if the paper is just cleverly disguised BS with pumped up numbers to get grants, and so on. Personally, I'm bearish about most deep learning papers for this reason. I'm not driven by a particular task/problem, so when I'm reading ML papers, it is primarily for new insights and ideas. Correspondingly, I prefer to read papers which have new perspectives on the problem (irrespective of whether they achieve SOTA performance). From what I've seen, most of the interesting (to me) ideas come from slightly adjacent fields. I care far more about interesting & elegant ideas, and benchmarks to just sanity-check that the nice idea can also be made to work in practice. As for the obsession with benchmark numbers, I can only quote Mark Twain: “Most people use statistics like a drunk man uses a lamppost; more for support than illumination.”
- bonoboTP 7y ago(Not a Twain quote: https://quoteinvestigator.com/2014/01/15/stats-drunk/ https://quoteinvestigator.com/2014/01/15/stats-drunk/ )
- wodenokoto 7y agoIs there a text that will explain the difference between a paper, an article, a manuscript, a monograph and all the other words often used to describe different kinds of written scientific material?
- inimino 7y agoA dictionary? ;-) But seriously, if you want to know the differences look up the etymologies and then keep your eyes open for how each term is used in practice. That's all there is to it.
- wodenokoto 7y agoMy dictionary defines manuscript as something written by hand rather than typed. Not exactly something that illuminates the usage in scientific literature
- inimino 7y agoDoesn't it though? If you look at the etymology, manus (hand) + script (write) directly gives you "hand-written", and then a hand-written document is more likely to be the work of a single author, more likely to refer to an original than a copy, and more likely to be old (before printing, typewriters, or computers). Everything else is just associations that a word picks up over time or in a specific community.
- tokai 7y agoTry this resource https://www.abc-clio.com/ODLIS/odlis_about.aspx https://www.abc-clio.com/ODLIS/odlis_about.aspx
- puhi 7y agoHow do i find all the interesting papers? Like i like to read about things like: ML, Scaling, Filesystem, Databases, Algorithms etc. I do get a lot of input through hn, friends, youtube, blogs but i'm not getting my papers from direct sources. I don't have anything like nature or so laying around either.
- cosmic_quanta 7y agoIt depends on the field, but for my fields, most new work is first published as pre-prints for free on arXiv (not peer reviewed yet!). There are new entries every day. You may want to check it out.
- throwlaplace 7y agohttps://blog.acolyer.org/ https://blog.acolyer.org/
- puhi 7y agoYes! Thats what i'm reading for 'blog' :)
- Mvhsz 7y agoTry to find a survey paper or review article [1] on the field you're interested in. They summarize the current state of the art in a field and link to the relevant papers. If the linked papers are behind a pay wall then you can use arXiv as recommended in another comment by searching the title/authors. The field usually wouldn't be as broad as "databases" but you could probably find one on "distributed wide column stores". I think they're usually published by grad students before they pick their thesis topic. [1] https://en.m.wikipedia.org/wiki/Review_article https://en.m.wikipedia.org/wiki/Review_article
- frayesto 7y agoYou can also go here for almost all papers https://whereisscihub.now.sh/go https://whereisscihub.now.sh/go
- 7y ago
- Noxmiles 7y agoI want to know the content of the paper before reading the paper. The struggle is real...
- ooklala 7y agoRead the abstract?
- tjchear 7y agoCan we apply the steps to reading source code? Here's my take on adopting it to reading code: 1. Read readme if available, read the list of source files to get a sense of how the project is modularized. Identify the entry point. Identify type of program from main entry point: is it a server, a CLI, or a graphical app? 2. Run call graph analysis tool if you have it, so you can study callgraph tree starting from main entry point. Read just the function names and start making notes of how the execution works at various levels, e.g does it read input then enter an infinite loop, does it wait on network packets, does it use update/render loop, etc. Also make note of whether a function is trivial/non-trivial based on quick glance at the code. 3. Ignore the trivial ones, and read the non-trivial ones in detail. Make note of the algorithm, data structures, and dependencies.