About
About Annotated Papers
This site contains detailed annotations of research papers that I have found worth understanding closely. Each annotation combines the source paper, explanatory notes, runnable code, and interactive figures.
Purpose
Research papers are concise records of new results. They often assume substantial background knowledge and leave implementation details to supplements or codebases. That economy is useful for communicating among specialists, but it can make careful study unnecessarily difficult.
These annotations slow the reading process down. The goal is not to replace the original paper or reduce it to a summary, but to make the paper easier to work through in full. The format follows the example of The Annotated Transformer.
Method
Each annotation is organized around three related forms of evidence:
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The paper
The argument, equations, and architecture are explained in sequence. Notation is kept close to the source so readers can move between the annotation and the paper.
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The implementation
Code excerpts come from real, runnable reimplementations rather than pseudocode. Variable names follow the paper, algorithm numbers are cited inline, and tensor shapes are checked throughout the code.
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The figures
Interactive figures are used where manipulating a mechanism directly is more informative than another paragraph of description.
Code and provenance
The implementations are written for pedagogical clarity and include unit and integration tests. Each annotation pins its reference repository to a specific commit. This makes it possible to inspect the exact version used by the site, clone it, and run the examples locally.
Current annotation
The Annotated AlphaFold2
The first annotation covers AlphaFold2, including the Evoformer, Invariant Point Attention, FAPE, and the Structure Module. It is paired with minAlphaFold2, a roughly 8,000-line PyTorch implementation of about 95% of the paper.