Over time, I curated some awesome resources for (technical and non-technical parts of) research; most of which specific to Computer Vision, Machine Learning. I update this occasionally.
Research Culture and Community Norms from UC Berkeley has important discussions relating to the grad school experience. It’s about the (often undiscussed) psychological, emotional counterpart of an academic journey. Prof. Justine Sherry also has a similar reading list.
- Good Citizen of CVPR event held at CVPR 2018; of course, useful for all conference submissions!
- Reproducibility in ML: Tools and Best Practices blog.
- Scientific Writing by Stanford University on Coursera
- This video by Prof. Michal Lipson!
- How To Write A Good Paper by Prof. Jitendra Malik! In extension, this is from Good Citizen of CVPR.
- This paper on how to read a paper by S. Keshav, now a Professor of CS at University of Cambridge. I’d definitely recommend reading this!
- Reading primary literature by Cait Kirby. It’s a short 1 page infographic on how to read papers.
- How to seriously read a scientific paper by Science Magazine!
- Talks that don’t suck by Cyrill Stachniss.
- Amy Tabb’s blog post on how to review technical papers when you’ve been not taught how!
- Reviewing for dummies blog post on Ameya Prabhu’s webpage.
- CVPR 2020 tutorial on How to Write a Good Review!
- CVPR 2021 has training information for reviewers!
- 23 things I didn’t learn in grad school thread on twitter by D. Sivakumar.
- The very popular time management post, Calendars. Not to-do lists by Devi Parikh! A short trail run of this while applying to PhD positions proved it very useful for me. Plan to implement it more in the future.
- Machine Learning Glossary by Yann Dubios that summarizes important terms and concepts of machine learning.
Text me on twitter if you want me to add links here that you found useful!