Pith Number
pith:5HLJ25ZF
pith:2024:5HLJ25ZFPTUB6YXVO4N4TYLNG2
not attested
not anchored
not stored
refs pending
Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data
arxiv:2404.10177 v2 · 2024-03-20 · cs.CV · cs.AI · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{5HLJ25ZFPTUB6YXVO4N4TYLNG2}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Cited by
Receipt and verification
| First computed | 2026-07-05T08:46:54.395177Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e9d69d77257ce81f62f5771bc9e16d36935801fb62fcc1259ffad6979f0d998a
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/5HLJ25ZFPTUB6YXVO4N4TYLNG2 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e9d69d77257ce81f62f5771bc9e16d36935801fb62fcc1259ffad6979f0d998a
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "aad3ab28aa1b2faf0b31daa26bcbf815c86ce9df40779aed326bddc0ee43bc9d",
"cross_cats_sorted": [
"cs.AI",
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CV",
"submitted_at": "2024-03-20T14:22:12Z",
"title_canon_sha256": "392e4c39756cfa4f463dfce188fae0bda37954811c988d86559909c7b85e3377"
},
"schema_version": "1.0",
"source": {
"id": "2404.10177",
"kind": "arxiv",
"version": 2
}
}