Art of Reading Scientific Research Papers

Building a habit of reading research papers and everything you need to get started!

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Definition of a research paper

A research paper is a highly congested and bland manuscript that compiles a thorough understanding of a problem/topic, a proposed solution/research along with the conditions under which it was deduced/carried out, the efficacy of the solution/research, and potential loopholes in the study.

What it is NOT

There is a common notion that a research paper is a well-informed summary of a problem or topic written by means of other sources. Don’t mistake it for a book or an opinionated account of an individual’s interpretation of a particular topic.

Why should one read research papers?

What I find fascinating about reading a good research paper is that one can draw on a profound study of a topic and engage with the community on a new perspective to understand what can be achieved in and around that topic.

  1. Exploration — Whether you have a pinpointed agenda or not, there is a very high chance that you will stumble upon an edge case or a shortcoming that is worth following up. With persistent efforts over a considerable amount of time, you can learn to utilize that knowledge into making a living.
  2. Research and review — One of the main reasons for writing a research paper is to further the development in the field. Researchers read papers to review them for conferences or to do a literature survey of a new field. For example, Yann LeCun’s paper on integrating domain constraints into backpropagation set the foundation of modern computer vision back in 1989 and with decades of research and development work, we have come so far where we are perfecting problems like object detection and optimizing autonomous vehicles.

Goals for reading a paper — What should you read about?

The first thing to do is to figure out the motivation to read the paper. There can only be two scenarios in which you’ll want to read a paper:

  1. Scenario2 — You want to keep abreast of the developments in a host of areas, say how a new deep learning architecture has helped us solve a 50-year old biological problem of understanding protein structures. This is often the case for beginners or for people who consume their daily dose of news from research papers(yes, they exist!).

ML Reproducibility Challenge

In addition to these generic goals, if you need an end goal for your habit-building exercise of reading research papers, you should check out the ML reproducibility challenge.

https://openreview.net/group?id=ML_Reproducibility_Challenge/2020

Getting Started — How to find the right paper?

In order to get some ideas around this, I reached out to my friend, Anurag Ghosh who is a researcher at Microsoft. Anurag has been working at the crossover of computer vision, machine learning, and systems engineering.

https://anuragxel.github.io/
  • Read a few good book or detailed blog posts around that topic and start diving deep by reading the papers referenced in those resources.
  • Look for seminal papers around that topic. These are papers that report a major breakthrough in the field and offer a new method perspective with a huge potential for subsequent research in that field. Check out papers from the morning paper, CVF — test of time award/Helmholtz prize(if interested in computer vision).
  • Check out books like computer vision by Computer Vision: Algorithms and Applications by Richard Szeliski and look for the papers referenced there.
  • Have/Build a sense of community. Find people who share similar interests, join groups/subreddits/discord channels where such activities are promoted.
https://www.reddit.com/r/MachineLearning/
http://www.arxiv-sanity.com/
https://research.google/

Method of reading a paper

After you have stocked your to-read list, then comes the process of reading these papers. Remember that NOT every paper is useful to read and we need a mechanism that can help us quickly screen papers that are worth reading.

Three pass approach

  1. The first pass — It is a quick scan to capture a high-level view of the paper. Read the title, abstract, and introduction carefully followed by the headings of the sections and subsections and lastly the conclusion. It should take you no more than 5–10 mins to figure out if you want to move to the second pass.
  2. The second pass — is a more focused read without checking for the technical proofs. You take down all the crucial notes, underline the key points in the margins. Carefully study the figures, diagrams, and illustrations. Review the graphs, mark relevant unread references for further reading. It helps you understand the background of the paper.
  3. The third pass — reaching this pass denotes that you’ve found a paper that you want to deeply understand or review. The key to the third pass is to reproduce the results of the paper. Check it for all the assumptions and jot down all the variations in your re-implementation and the original results. Make a note of all the ideas for future analysis. It should take 5–6 hours for beginners and 1–2 hours for experienced readers.

Different tools/software to keep track of your pipeline of papers

If you’re sincere about reading research papers, your list of papers will soon overgrow into an overwhelming stack that is hard to keep track of. Fortunately, we have software that can help us in setting up a mechanism to manage our research.

https://www.mendeley.com/?interaction_required=true
https://www.zotero.org/
https://www.notion.so/My-paper-pipeline-ec3ff02ce9c641d2953f6cdbc431a55a

⚠️ Symptoms of reading a research paper

Reading a research paper can turn out to be frustrating, challenging, and time-consuming especially when you’re a beginner. You might face the following harmless symptoms:

  • Finding yourself pushing too hard to understand the math behind those proofs.
  • Beating your head against the wall to wrap it around the number of acronyms used in the paper. Just kidding, you’ll have to look up those acronyms every now and then.
  • Being stuck on one paragraph for more than an hour.

Key Takeaways

We should be all set to dive right in. Here’s a quick summary of what we have covered here:

  • Read research papers to develop a deep understanding of a topic/problem and then you can either review papers as part of being a researcher, explore the domain and the kind of problems to build a solution or startup around it or you can simply read them to keep abreast of the developments in your domain of interest.
  • If you’re a beginner, start with exploration to soon find your path to goal-oriented research.
  • In order to find good papers to read, you can use websites like arxiv-sanity, google research, and subreddits like r/MachineLearning.
  • Reading approach — Use the 3-pass method to read a paper.
  • Keep track of your research, notes, developments by using tools like Zotero/Notion.
  • This can get overwhelming in no time. Make sure you start off easy and increment your load progressively.

Web & Data Science Instructional Designer | YouTuber | Writer https://www.youtube.com/c/DataSciencewithHarshit

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