Pith Number
pith:LBLZ5JPH
pith:2019:LBLZ5JPH2QL6EV7FH7PLZ5X4S5
not attested
not anchored
not stored
refs pending
Really should we pruning after model be totally trained? Pruning based on a small amount of training
arxiv:1901.08455 v1 · 2019-01-24 · cs.NE
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{LBLZ5JPH2QL6EV7FH7PLZ5X4S5}
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.
Receipt and verification
| First computed | 2026-05-17T23:55:35.395056Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
58579ea5e7d417e257e53fdebcf6fc974dcd1ed860272363aae8dfaea1ffc46d
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LBLZ5JPH2QL6EV7FH7PLZ5X4S5 \
| 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: 58579ea5e7d417e257e53fdebcf6fc974dcd1ed860272363aae8dfaea1ffc46d
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "5e5ed18544a66a85ab6a5742b7560fbac2ade2a1b8c72109376de799af3c070f",
"cross_cats_sorted": [],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.NE",
"submitted_at": "2019-01-24T15:30:54Z",
"title_canon_sha256": "5b4dd38646e94e2433e9b89ed9afd14383c7637807042e8ff3b27b5a061792da"
},
"schema_version": "1.0",
"source": {
"id": "1901.08455",
"kind": "arxiv",
"version": 1
}
}