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Reproduction bundle — Credit Assignment via Neural Manifold Noise Correlation
Independent reproduction of ICML 2026 paper #13080 (arXiv 2601.02636, OpenReview RyxUQr2l4k).
The paper released no code; everything here was written from the paper text.
Contents
| Path | What it is |
|---|---|
nmnc/ |
Implementation: incremental PCA, CIFAR/AlexNet models, weight mirror, the 5 claim experiments |
modal_app.py |
Every experiment as a Modal GPU function (this is the entry point) |
results/ |
Raw per-run JSON from all Modal runs |
figures/ |
Aggregated tables + interactive Plotly figures |
poster/ |
Reproduction poster (posterly) |
analyse.py |
Turns results/ into figures/ |
paper.txt |
Extracted paper text used as the implementation spec |
Rerun
pip install modal torch torchvision numpy scipy datasets
modal run modal_app.py # cache CIFAR-10 to the shared volume
modal run modal_app.py::claim1 --seeds 3 --epochs 100
modal run modal_app.py::claim4 --seeds 2 --epochs 30
modal run modal_app.py::claim5 --trials 400
modal run modal_app.py::prep_imnet # cache ImageNet-100
modal run modal_app.py::claim2 --seeds 2 --epochs 16
modal run modal_app.py::claim3
python analyse.py
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