Paper Reading List
machine learning systems and multimodal (and blogs!)
EDIT (03/27/25): Repurposing this post to share more general resources I’ve curated over the past year!
⚙️ Systems: Link to Notion
📺 Multimodal: Link to Notion
Blogs: Link to Notion
Technical Guides/Resources: Link to Notion
Table of Contents
Intro
I found it incredibly difficult to find credible and well authored technical writing when beginning my journey in ML a little over a year ago. In hopes that this will help you on your journey, I’m sharing some of my favorite posts and authors.
These are just some of the one’s I’ve checked out, so I highly recommend that you take a look at some of the other posts by these authors, which may better align with your areas of interest.
Please let me know of any other awesome authors I’ve missed!
Authors and Posts
Alex Irpan
The Tragedies of Reality Are Coming for You
Benjamin Spector
Benjie Holson
So You Want To Do Robots, Part 2: What do you need to invent?
Eric Jang
Software and Hardware for General Robots
Horace He
Making Deep Learning Go Brrrr From First Principles
James Betker
The “it” in AI models is the dataset.
Jan Leike
What is the alignment problem?
Why I’m optimistic about our alignment approach
Jason Wei
137 emergent abilities of large language models
Ken Liu
Lillian Weng
How to Train Really Large Models on Many GPUs?
Large Transformer Model Inference Optimization
Neel Nanda
Concrete Steps to Get Started in Transformer Mechanistic Interpretability
Nishanth Kumar
Will Scaling Solve Robotics?: Perspectives From Corl 2023
Rich Sutton
Simon Boehm
Data-Parallel Distributed Training of Deep Learning Models
Pipeline-Parallelism: Distributed Training via Model Partitioning
Yi Tay
Training great LLMs entirely from ground up in the wilderness as a startup
Extras (for students/research)
Akihiro Matsukawa
Chris Olah
Do I Need to Go to University?
Eric Jang
Lessons from AI Research Projects: The First 3 Years
My Internship Experiences at Pixar, Google, and Two Sigma
Jason Wei
Patrick Kidger
How to handle a hands-off supervisor
Just know stuff. (Or, how to achieve success in a machine learning PhD.)
Rose Wang
Dear future undergraduate researcher
Trenton Bricken
Reflections two years into the PhD
Tristan Hume