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<title>kaft-neurips.git/figures/gen_fig1_diagnostic.py, branch main</title>
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<link rel='alternate' type='text/html' href='https://git.blackhao.com/kaft-neurips.git/'/>
<entry>
<title>Initial release: GRAFT (KAFT) — NeurIPS 2026 submission code</title>
<updated>2026-05-05T04:05:16+00:00</updated>
<author>
<name>YurenHao0426</name>
<email>blackhao0426@gmail.com</email>
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<published>2026-05-05T04:05:16+00:00</published>
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<id>bd9333eda60a9029a198acaeacb1eca4312bd1e8</id>
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Topology-factorized Jacobian-aligned feedback for deep GNNs. Includes:
- src/: GraphGrAPETrainer (KAFT) + BP / DFA / DFA-GNN / VanillaGrAPE baselines
        + multi-probe alignment estimator + dataset / sparse-mm utilities.
- experiments/: 19 runners reproducing every figure / table in the paper.
- figures/: 4 generators + the 4 PDFs cited in the report.
- paper/: NeurIPS .tex and consolidated experiments_master notes.

Smoke test: 50-epoch Cora GCN L=4 gives BP 77.3% / KAFT 79.0%.
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Topology-factorized Jacobian-aligned feedback for deep GNNs. Includes:
- src/: GraphGrAPETrainer (KAFT) + BP / DFA / DFA-GNN / VanillaGrAPE baselines
        + multi-probe alignment estimator + dataset / sparse-mm utilities.
- experiments/: 19 runners reproducing every figure / table in the paper.
- figures/: 4 generators + the 4 PDFs cited in the report.
- paper/: NeurIPS .tex and consolidated experiments_master notes.

Smoke test: 50-epoch Cora GCN L=4 gives BP 77.3% / KAFT 79.0%.
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