How to read AI/ML research papers – Analytics India Magazine

Research papers are the wellspring of ideas pushing the frontiers of cutting edge technologies. Scholars around the globe rely on platforms like arXiv, JSTOR, Reddit, PapersWithCode to get up to speed on the latest in AI and data science. But let’s face it, research papers are a hard nut to crack. The time and effort you put into unpacking a research paper might go down the drain unless you have a method/reading strategy in place.To that end, we have put together a tutorial to help you get the best out of research papers.

Locking in the right paper/s

Whether you want to learn about Transformers or Hidden Markov Model, finding the right papers is the first step. You can choose relevant papers by number of citations, authors, impact etc. Sorting the reading material as primary, secondary and tertiary will save you a lot of time and effort and optimise your research. Richmond Alake, computer vision engineer at Loveshark, suggested themes based on ML/DS jobs to get you started.

Skim to gain context

Before you dive into the research paper, you should first understand the contents of the paper by taking a quick look at the title, abstract, and conclusion. Skimming the paper will give you an idea about the author’s theories and methodology, and if the paper aligns with your goal.

Get familiar

The next step is to get a feel for the subject matter explored in the paper. Reading the introduction and glancing through the visual aids (graph, diagrams, figures) in the research paper set expectations and help you ease into the complex areas of the research. The introduction provides an overview of the problem statement, research scope, goals, previous research efforts, and methodologies.

The visual representations of data put the research paper in the context. Tables and graphs codify the information in an easy to understand format.

Deep reading

You need to avoid any complicated arithmetic or technical formulations and skip over any words or definitions you don’t understand. Keep a note of these terms, techniques and algorithms, and revisit it later.

The primary goal is to gain a broad understanding of what is covered in the paper. Approach the paper with fresh eyes, beginning with the abstract and ending with the conclusion, but make sure to take breaks between sections. It’s also a good idea to keep a notepad to jot down the key insights and takeaways, as well as unfamiliar terms and concepts.


The final step involves going over the unfamiliar terms, terminologies, concepts, and algorithms noted down earlier. The final stage focuses on using external material to understand the unfamiliar concepts in the paper.

You can use resources like Machine Learning Subreddit and Deep Learning Subreddit to enrich your understanding. Lastly, writing a precis of the paper in your own words will help you get an idea of how much you have learned. Deliberate practice is the key in getting the concepts down cold- rinse and repeat to fill the knowledge gaps.

PS: Watch Andrew Ng’s lecture on getting the most out of research papers.

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