An alternate resource for reading papers can also be found here
These are the notes from the amazing lecture by Andrew Ng, full video can be viewed here
- Compile a list of resources
- Papers from arxiv and conferences
- Journals
- Medium/Github posts
- Articles/Blogs
- Skim through the list of resources
- Make a table, where rows represent lists of papers and a column for a metric of how much you understood through skimming (10-100%)
- Now read the one with the lowest value of the metric, try to understand the paper, if you can't, go to the references, and read those till you get a basic idea of the paper
- Keep doing this till you have a basic knowledge of the papers
- Then select the papers you feel is worthy enough to be completely read
- Reading around 5-20 papers, you'll have some basic idea of the field and for implementing the works
- Reading around 50-100 papers, you'll have a deep understanding to do in-depth research (it does not mean you have mastered the field 😄)
- Make a table, where rows represent lists of papers and a column for a metric of how much you understood through skimming (10-100%)
Take multiple passes through the papers
- First Pass
- Read Titles, Abstract, and Figures (only figures can sometimes summarize the entire paper)
- Second Pass
- Read more carefully the Introduction, Conclusion, Figures and then skim through the rest (skip related work if you're not familiar with it in the second pass)
- Third Pass
- Read but skip or skim the Math
- Fourth Pass
- Whole thing but skip parts that don't make sense
Ask yourself these questions while reading the paper
- What are the authors trying to accomplish in this work?
- What were the key elements of the approach in this work?
- What can you use yourself?
- What other references do you want to follow?
- Top Tier Conferences like NeurIPS, ICLR, CVPR, ICRA, RSS, IROS (more on this here)
- Subreddits
- Paper Reading Groups, Communities and Friends
To understand the Maths behind the paper
- Read a few passes and make detailed notes
- Try to rederive the math from scratch on blank paper
- If you can do this, then you can learn to derive your own novel algorithms
- Eg. People from the art community sit in the art museum and they copy the work of the masters
- If you can do this, then you can learn to derive your own novel algorithms
Download and run the open-source code and try to reimplement it from scratch
- Keep reading papers consistently
- In doing so you won't gain expertise in one day, you won't get a lot of knowledge from reading one paper a weekend. But if you keep doing this for a year, you'll reach somewhere
- One great project is better than many lame projects
- Focus on the team (people you interact with)
- Maintain a work-life balance