I mean, there are pros and cons to reading a lot of papers quickly vs reading a few papers intimately.Īs you stated, if you only read a paper for 15 minutes, you're really only going to pick up on a few key points. I found there are very few papers that are worse spending 2 days on, except for the most related work or if you are trying to reproduce a work. Reading one paper in detail is only useful if you really need to understand it for direct application. Or if I want to publish a paper at a new conference, I will skim through the previous year's accepted paper to see what they look likes to get a sense of the "style" of a conference. Skimming through a lot of paper quickly is useful to get a sense of what's done or what people care about in a field you are unfamiliar with.įor example, if I want to work on a specific topic, I might skim through a lot of papers, just focusing on the evaluation to see what's the expected level of thoroughness in the experiment (e.g., number of test projects, accuracy, etc.). If I work in computer vision, there is no point in spending 2 days on an NLP paper, but that doesn't mean you should not look at any NLP papers. I have several levels of "reading" from "I read the title and it looked interesting" to "I tried the source code." All of these levels of "reading" are useful. Both superficial and thorough reading is important and serves different goals. I don't agree that fully understanding a single "key" paper is necessarily the best. Īlso, come join our study group and subscribe on telegram here: \_gan I've been posting easy to read summaries from the papers I read thoroughly (usually takes me about 2-3 hours per paper) on my blog twice a week for more than half a year, and if you work with GANs or want to learn about generative models it might be a useful resource. Hence, I started writing down the main ideas and findings of the papers I read to quickly refresh the main points in my head when needed. Interestingly, I found that just reading the papers is not enough though, because you probably won't remember anything in 2 weeks, especially with this amount of new information that you learn just in case. From those papers, I pick one (or none at all) that seems the most interesting/relevant and read it more attentively. ![]() This field moves so fast that it is easy to get lost if you stop following the new papers for a couple of weeks, hence I try to at least glance at all of the new papers from my field (I work with GANs) on a daily basis and read the abstract/check out the results for maybe 2-3 papers. Just want to chime in since this conversation is so relevant to what I do. I take issue though when someone claims they’ve “read” something when all they’ve done is gone through the abstract, and glanced through it. I’m a firm believe that reading, comprehending and fully understand 1 single “key” paper from whatever field you’re studying, is a much better investment of your time than skimming through 100 regurgitated ideas.Įdit: guys just to clarify, I do believe in skimming abstracts and looking for interesting papers. I’m sorry this is turning into an rant, it just really grates my nerves when people say they read something and in reality all they did was look at the abstract. So we decide to have an impromptu discussion on it and Jesus Christ, I swear the only thing he read was the abstract and maybe glanced at the network architecture. Meanwhile, in the daily meetings we have I mention the paper and how we should try and use some of their components in our own work, and someone says, “oh ya, I read that in like 15 mins”. I’ve got a few colleagues who always claim to be reading papers, but the way they “read” is so damn superficial.Īs an example, I had just finished fully reading/comprehending a paper, and I won’t lie, took me a solid couple days to understand everything fully and reading things multiple times. ![]() Metacademy is a great resource which compiles lesson plans on popular machine learning topics.įor Beginner questions please try /r/LearnMachineLearning, /r/MLQuestions or įor career related questions, visit /r/cscareerquestions/ ![]() Please have a look at our FAQ and Link-Collection Rules For Posts + Research + Discussion + Project + News on Twitter Chat with us on Slack Beginners:
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