Author here, and Postdoc with Hugo Larochelle and Chris Pal.
I saw the LocalWiki last week, and it inspired me to make a learning tool based on "local" research papers. Many ML researchers write code that relies on a long history of research in ML and applied math. But which papers are you actually building on?
You can paste a GitHub repo and Standing on will read its Python code match pieces of it back to the research papers from which it arose.
nanoGPT, for example has 15 Python files and links to 13 papers including Adam, AdamW, LayerNorm, GELU, FlashAttention, etc. If you go back further, you get Adam and then AdaGrad (2011), Zinkevich's online convex programming (2003) and finally you wind up with Nash (1950) and Kakutani's fixed-point theorem (1941).
It's a very simple symbol table map with 4,800 classes and functions in torch, transformers, diffusers, timm, sklearn and others to the papers they implement. I used Claude and Codex to label the symbols and named each paper's parents, and check them against our ML research map, Lacuna.
We only support python and machine learning papers only for now. You can export to BibTeX, CSV and Markdown.
2 comments
[ 7.7 ms ] story [ 36.9 ms ] threadI saw the LocalWiki last week, and it inspired me to make a learning tool based on "local" research papers. Many ML researchers write code that relies on a long history of research in ML and applied math. But which papers are you actually building on?
You can paste a GitHub repo and Standing on will read its Python code match pieces of it back to the research papers from which it arose.
nanoGPT, for example has 15 Python files and links to 13 papers including Adam, AdamW, LayerNorm, GELU, FlashAttention, etc. If you go back further, you get Adam and then AdaGrad (2011), Zinkevich's online convex programming (2003) and finally you wind up with Nash (1950) and Kakutani's fixed-point theorem (1941).
It's a very simple symbol table map with 4,800 classes and functions in torch, transformers, diffusers, timm, sklearn and others to the papers they implement. I used Claude and Codex to label the symbols and named each paper's parents, and check them against our ML research map, Lacuna.
We only support python and machine learning papers only for now. You can export to BibTeX, CSV and Markdown.