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.
How to research
Do science right!
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.
Writing Advice
- 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.
Reading Papers
- 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!
Giving Good Presentations!
- Talks that don’t suck by Cyrill Stachniss.
Reviewing Papers
- 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!
General Advice
- 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.
Applying to Grad School in CS
Krishna Murthy writes about grad school application advice focusing on different factors that give a holistic view on choosing a school and advisor based on what is important to you.
A definitive guide to what do I look for/ask for in a PhD advisor curated by Andrew Kuznetsov.
The actual research
Blogs
- 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!