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"schema_version": 1,
"title": "Repro - A theory of learning data statistics in diffusion models, from easy to hard",
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"space_id": "tpls/repro-diffusion-data-stats",
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"updated_at": "2026-07-21T20:52:26+00:00",
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"title": "Repro - A theory of learning data statistics in diffusion models, from easy to hard",
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"slug": "claim-1-diffusion-denoisers-trained-on-natural-images",
"title": "Claim 1 — Diffusion denoisers trained on natural images…",
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{
"slug": "claim-2-for-a-k-th-order-cumulant-target",
"title": "Claim 2 — For a k-th order cumulant target,…",
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{
"slug": "claim-3-if-the-number-of-samples-n-d",
"title": "Claim 3 — If the number of samples n(d)…",
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"slug": "claim-4-in-a-mixed-cumulant-latent-model-pair-wise",
"title": "Claim 4 — In a mixed-cumulant latent model, pair-wise…",
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"title": "Claim 5 — When latent variables underlying the pair-wise…",
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{
"slug": "claim-6-unconstrained-sgd-exhibits-contraction-dynamics-toward",
"title": "Claim 6 — Unconstrained SGD exhibits contraction dynamics toward…",
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{
"slug": "evaluation-methodology",
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