How to Read Papers Efficiently?

Wed, 24 Feb 2021 00:00:00 +0000

Efficient paper reading procedure

Do NOT read article from beginning to end!!!

Phase 1: Surveying the article

Feel free to stop reading the article at any point

Read the title and keywords (these are probably what got you look at the paper)
- If these don’t interest you at all ➡️ stop!
Read the abstract (most important part of the paper)
Read the conclusions

Phase 2: Reading the article

Look at the tables and figures (including captions)
- This is really what was done in the work
- It will help you decide, whether you want to dig in or not
Read the introduction carefully
- This is the background needed and why the study was done.
- Introduction in research paper is one of the most important point.
  - It often formulates in words what the paper is about, and what contribution the paper makes. (like a longer abstract)
  - It is informative and help us to understand the paper in a high level.
Read the results and discussion
- This is the real heart of the paper. Here you will spend most of the time
- Keep asking: “Are the authors demonstrating that their proposal/solution work?”
  - Try to prove them wrong or get convinced that solution works
Note: Sometimes Results section is included in Experiments section.
Read the experimental
- This is how they did the work
- You only get to this point if you are really interested and need to understand exactly what was done to better understand the meaning of the data and its interpretation.

Phase 3: Write some notes so you don’t have to re-read the whole paper again

The faintest writing is better than the best memory! 好记性不如烂笔头！

Reference

🔥👍 How to Read a Paper Efficiently (By Prof. Pete Carr): a great tutorial teaching you how to read papers. Strongly recommend!
如何高效读论文？
How I Read a Paper: Facebook’s DETR (Video Tutorial)

Advice on Reading Research Papers (by Prof. Andrew Ng)

Wed, 24 Feb 2021 00:00:00 +0000

Here we’ll summarize two major recommendations given by Prof. Andrew Ng in his CS230 Deep Learning course:

Reading research papers
Advice for navigating a career in machine learning

Reading research papers

List of papers

Compile a list of papers

Try to create a list of research papers, medium posts and whatever text or learning resource you have
Skip around the list
- Read research papers in a parallel fashion; meaning try to tackle more than one paper at a time
- Concretely, try to quickly skim and understand each of these paper and do not read it all, maybe you read 10–20% of each one and probably that will be enough to give you a high-level understanding of the paper in hand.
- After that, you may decide to eliminate some of these papers or just go over one or two them and read them fully.
Amount of papers
- 5–20 papers
  - Probably enough basic knowledge of the specific domain
  - But maybe not enough to research or be at the cutting-edge.
- 50–100 papers
  - Probably have a very good understanding of the domain application

How do you read ONE paper?

Do NOT start reading the paper from the first to the last word. Instead, take multiple passes through the paper

Read the Title, the abstract and the figures
- By reading the title, abstract, the key network architecture figure, and maybe the experiments section, you will be able to get a general sense of the concepts in the paper.
- In deep learning, there are a lot of research papers where the entire paper is summarized in one or two figures without the need to go hardly through the text.
Read the introduction + conclusions + figures + skim the rest
- The introduction, the conclusions and the abstract are the places where the author(s) try to summarize their work carefully to clarify for the reviewer why their paper should be accepted for publication.
- Skim the related work section (if possible), this section aims to highlight work done by others that somehow ties in with the author(s) work.
Read the paper but skip the math
Read the whole thing but skip the parts that don’t make sense

When reading a paper, try to answer the following questions:

What did the author(s) try to accomplish?
What were the key elements of the approach?
What can you use yourself?
What other references do you want to follow?

If you can answer these questions, hopefully, that will reflect that you have a good understanding of the paper.

It turns out as you read more papers, with practice you get faster. Because a lot of authors use common formats when writing papers.

Deeper understanding

Math

Try to rederive it from scratch. Although, it takes some time but it’s a very good practice.

Code

Lightweight: Download open-source code (if you can find it) and run it.
Deeper: Reimplement from scratch. if you can do this, that’s a strong sign that you have really understood the algorithm in hand.

To keep getting better

The most important thing to keep on learning and getting better is to learn more steadily rather than having a focus-intensive activity.

It’s better to read two papers a week for the next year than cramming everything over a short period of time.

Advice for navigating a career in machine learning

Just focus on doing important work and consider your job as a tactic and a chance to do useful work.

A very common pattern for successful machine learning engineers, strong job candidates, is to develop a T-shaped knowledge base

have a broad understanding of many different topics in AI and
very deep understanding in at least one area.

To build the horizontal piece

A very efficient way to build foundational skills in these domains is through courses and reading research papers.

To build the vertical piece

You can build it by doing related projects, open-source contributions, research and internships.

General advice

Learn the most

tend to choose things to work on that allow you to learn the most.
Do important work

work on worthy projects that moves the world forward.
Try to take machine learning to traditional industries

Reference

Career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University
Advice on building a machine learning career and reading research papers by Prof. Andrew Ng - A summary for Prof. Andrew Ng’s lecture

Read Papers | Haobin Tan

How to Read Papers Efficiently?

Efficient paper reading procedure

Phase 1: Surveying the article

Phase 2: Reading the article

Phase 3: Write some notes so you don’t have to re-read the whole paper again

Reference

Advice on Reading Research Papers (by Prof. Andrew Ng)

Reading research papers

List of papers

How do you read ONE paper?

Deeper understanding

Math

Code

To keep getting better

Advice for navigating a career in machine learning

General advice

Reference