Scientific Writing Tips for ML Researchers
Hot “call for papers” season is right around the corner. Here is a list of scientific writing tips for all you ML researchers.
A checklist adopted from “Heuristics for Scientific Writing (a Machine Learning Perspective) - @zacharylipton
Made with ❤️ by @omarsar0
- An abstract is not for venting…
- The abstract: the problem, state-of-the-art, contributions, and results
- Avoid teasers, be more explicit
- Avoid generic statements…
- Aim to make a first impression with interesting observations
- Problem statement, then solution, followed by results
- Real world use cases are juicy and motivating
- Avoid digressions, especially negative ones
- Neatly organize your paper: sections, subsections, bullets, etc.
- Figures tell a story, no-figures should have the same effect
- Basics are boring, get to the meat of the matter (contributions)
- Anticipate questions, and answer them as best as you can
- It’s all about “we” not “I”
- Sentences in isolation should be “unambiguous” and “objective”
- Avoid boasting, if you are not 100% sure of your claims
- An opinion should start as “in our opinion,…”
- Long sentences usually spell disaster…
- Keep sentences as compact and clear as possible…
- Sentences should have a flow, like a song
- Intensifiers only weaken your argument
- Pay att. to relationship between your subjects, verbs and modifiers
- Don’t cite for sake of citing…only cite the most relevant works
- For readability, let citations roam freely all over your paper
- Cite, cite, cite, cite, and keep on citing for as much as is allowed