Pith Number
pith:BX6N7H4T
pith:2023:BX6N7H4TPYQM7JLQ5PLDEYF2TR
not attested
not anchored
not stored
refs pending
Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling
arxiv:2305.18375 v1 · 2023-05-28 · cs.LG · stat.ME · stat.ML
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{BX6N7H4TPYQM7JLQ5PLDEYF2TR}
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
· sign in to
claim
4
Citations
5
Replications
✓
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.
Receipt and verification
| First computed | 2026-07-05T06:15:00.230746Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0dfcdf9f937e20cfa570ebd63260ba9c4119b186abd34f3acd7ef06a4aa7b047
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BX6N7H4TPYQM7JLQ5PLDEYF2TR \
| 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: 0dfcdf9f937e20cfa570ebd63260ba9c4119b186abd34f3acd7ef06a4aa7b047
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "a507a12da89cbeb7d7a8e56e10cf9d76f0057361c0f63fdf02e3478efebd9a40",
"cross_cats_sorted": [
"stat.ME",
"stat.ML"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2023-05-28T05:38:28Z",
"title_canon_sha256": "08a981ed50b63280e534dad07d98aecc956f240cf25072b105f352c33419ac60"
},
"schema_version": "1.0",
"source": {
"id": "2305.18375",
"kind": "arxiv",
"version": 1
}
}