What papers are you reading? What's your process for reading papers?

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Would love to know what papers people are into at the moment.

Also, have you found techniques to help you digest papers efficiently?

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Iā€™m reading the following papers by Kamalika Chaudhuri:

  1. https://arxiv.org/pdf/2102.07048.pdf
  2. https://arxiv.org/pdf/2003.02460.pdf

I will be integrating computing the statistics on these papers into my W&B dashboard. Super excited for the results.

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Interesting! How do you pick the papers you want to read Diganta? Do you skim a bunch and commit to a few?

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For these 2 papers, I actually watched her talk and it struck a cord since I was ideating with a colleague on an idea which is correlated to her work.
I also read papers based on the trending reports at W&B, especially those under the Reproducibility Challenge.

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I just read ā€œOmnimatte: How to Detect Objects and Their Effectsā€ and wrote a blog post about it.

My process for reading papers depends on why Iā€™m reading it.

Looking for an interesting read:

  1. Read Abstract, Introduction & Conclusion
  2. Check online if someone has made a blog, YouTube video etc.

Looking to learn about new field / approach:

  1. Follow Google Scholar citations to like find around 5 largely cited papers
  2. Read Abstract, Introduction & Conclusion, in the most cited ones, read the Methodology & Results sections.
  3. Find the best, more recent Related Works section which paints a good picture of the field
  4. Choose a few to spend more time with :arrow_down:

Looking to implement / deeply understand a paper:

  1. Read Abstract, Introduction & Conclusion
  2. Read Related Works
  3. Read Methodology, Results & any blog posts / explainers online
  4. Read the source code if available / any open source implementation

I love hearing about how people find and read research papers.

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I am currently reading papers around semisupervised learning. To be specific, reading the SimCLR series.
I have written a blog recently on how to read papers and I specifically read papers using the same approach. It resonates a lot with what @scott shared depending on the level of interest, going about different passes. Hope others find it useful too.

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I select papers to read by browsing the trending section at paperswithcode.

For example, the most recent papers Iā€™ve been reading in depth are:

My reading process can be roughly summed up as:

  • I read the abstract online

  • If my interest is roused, I download the paper, read the introduction, skim over the method, and take a look at the figures and result tables

  • Unless I plan on reproducing the paper, Iā€™ll stop at this point.

  • I typically want to reproduce papers when:

    • They propose ā€˜simple in practiceā€™ yet highly effective methods
    • The experiments arenā€™t too computationally expensive and can be done on free cloud compute (aka my saviour, kaggle)
    • There arenā€™t any torch implementations already
    • I have a lot of free time

  • If I do decide to implement the paper:

    • I look for a blog post or a YouTube video that goes over the core concepts. (Yannic Kilcher is often my go to YT channel)
    • I buckle down and thoroughly read the paper, while making annotations and taking notes
    • Very occasionally, there are parts in papers which are a bit confusing, and I canā€™t find the answer I want after googling. In this case, I send an email to the paperā€™s authors.
    • Once I have all the theoretical details down, I start working on a kaggle notebook.
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I loved Morgan McGuireā€™s guide. This is from the view of a computer graphics researcher, however, I feel that many of the key lessons generalize quite well. Recommended! :smiley:

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