[R][D] Reading ML Papers - Workflow/Advice

Submitted by EndlessRevision t3_z0t72f in MachineLearning

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!

29

Comments

You must log in or register to comment.

Stock-Violinist6297 t1_ix7di3p wrote

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.

4

drivanova t1_ix7ia0q wrote

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

17

waa007 t1_ix7rfte wrote

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

Tips from Andrew Ng.

EDIT: source video class from Andrew Ng

4

Tgs91 t1_ix8vf0r wrote

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

3

drivanova t1_ix9rha7 wrote

Replying to drivanova (#598,980)

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.

5

Fabulous-Possible758 t1_ix9rz3s wrote

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.

1