How to read an ML paper
The anatomy of a machine-learning paper, S. Keshav's three-pass method, a procedure for decoding any method section, and the questions that separate a strong experimental claim from a weak one.
40 min
After this lesson you can
- Name the parts of an ML paper and what each is for
- Apply Keshav's three passes, and decide after each whether to go on
- Decode a method section into symbols, shapes and working code
- Interrogate an experiments section — baselines, ablations, seeds, compute, contamination
- Tell an arXiv preprint from a peer-reviewed paper, and cite the right version
In this lesson
- The anatomy of an ML paper
- The three passes
- Decoding a method section
- Reading the experiments critically
- Reproducing results
- Preprints and peer review
This lesson is part of Descent Pro
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