I have a few papers to read for research, and I'm not exactly sure how to start and how to go about reading/understanding. My goal is to read and understand the papers so that I can make comments and ask meaningful questions to get an understanding of the current research work. Here's what I have in mind of what I might do, based on advice from friends/professors:

Skim through paper and try to get a grasp of the general idea
Look through paper again more closely, annotating/taking notes.
- If there is a concept/idea I am not familiar with, make a note of that, then once done reading, go back and learn the concept. (mostly with respect to signals or concepts in ML I have not learned about through coursework yet)
Use notes from the previous step to come up with questions/comments that I can use to discuss
If time allows, a tip I heard from a prof about demonstrating understanding was to replicate the paper, so do something of the sort

Thoughts on this workflow? I haven't really read papers in the past, so any advice and comments on this workflow would be appreciated!

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drivanova t1_ix7ia0q wrote on November 21, 2022 at 9:50 AM

I think the way you read papers depends on the subfield.

For example, computer vision papers (papers that go into CVPR, ICCV etc) tend to be more empirical, meaning that you may want to spend more time on the experiments, watching out for potential failure cases, asking yourself if baselines considered are appropriate and in line with what people in the field do.

For more theory-oriented papers (AISTATS, ICML etc), I'd spend more time on the method, understanding assumptions and proofs of key results.

To familiarise myself with a paper and related work, I tend to use connected papers (https://www.connectedpapers.com) -- I find it super useful when getting into a subfield that's not exactly my area of research.

HTH

drivanova t1_ix9rha7 wrote on November 21, 2022 at 8:50 PM

PS: another thing I personally often do for papers from big conferences (ICML, Neurips etc) is watch the authors present their work on slideslive.com (most post pandemic papers have videos!). This is usually helpful to understand the motivation, high level ideas and key experiment results.

Stock-Violinist6297 t1_ix7di3p wrote on November 21, 2022 at 8:36 AM

Surely, good points to follow.In my opinion, firstly read out Abstract, conclusion and future work to get summary of whole paper.This would clear you out about the main crux of paper.Then, follow through intro methodology and so on.

emerging-tech-reader t1_ix7hpo7 wrote on November 21, 2022 at 9:41 AM

These are good points.

OP, your workflow sounds very similar to the one suggested by Barbara Oakley with a dash of Feynman technique.

I would also recommend trying Papers with code as sometimes looking at the code makes more sense.

waa007 t1_ix7rfte wrote on November 21, 2022 at 11:59 AM

Read the title, abstracts, figures, experiments
Go through conclusion, Figures and skip the rest
Read the rest but skip the math
Read whole but skip the parts that don't make sense

Tips from Andrew Ng.

EDIT: source video class from Andrew Ng

JackandFred t1_ixbjsjs wrote on November 22, 2022 at 5:10 AM

I hadn’t seen that before but I was going to say basically the same thing, if it’s coming from him it’s undoubtedly good advice

waa007 t1_ixbmst4 wrote on November 22, 2022 at 5:42 AM

Yes, it's from Andrew Ng, I add source video link above.

Tgs91 t1_ix8vf0r wrote on November 21, 2022 at 5:20 PM

After the first skim I like to go to YouTube and try to find a Paper Explained video for the paper if one is available. If it's a niche paper with no videos, look for the most important cited paper and go for that instead. Quality may vary, but it'll usually at least cover the important points and might catch something you missed on your first skim. I also like to do this with some work colleagues. We'll each take a paper, then explain them to each other and discuss

Fabulous-Possible758 t1_ix9rz3s wrote on November 21, 2022 at 8:53 PM

Going to point out a piece that seems to get overlooked: if there's a concept/idea you're not familiar with and it's a relatively new concept not found in textbooks, it's probably introduced and explored in one of the citations. Not all the citations will be useful but they are a basic dependency map of what you need to know to follow a field.

Secondly, depending on the age of the citation it is highly likely you can find the paper (or a draft version at least) for free on the author's website or they author will email it to you if you ask kindly.