Pith Number
pith:GI6DXVPN
pith:2022:GI6DXVPNSV6KGSDT6M2577M3VH
not attested
not anchored
not stored
refs pending
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
arxiv:2202.02643 v1 · 2022-02-05 · cs.LG · cs.AI · cs.CV
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{GI6DXVPNSV6KGSDT6M2577M3VH}
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
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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 | 2026-07-05T03:54:31.467382Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
323c3bd5ed957ca34873f335dffd9ba9e5a76b6aa811afeddfe59444f4415aa7
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/GI6DXVPNSV6KGSDT6M2577M3VH \
| 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: 323c3bd5ed957ca34873f335dffd9ba9e5a76b6aa811afeddfe59444f4415aa7
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "3def846b8f18d45b17318633be7028bb2d461a8087ff69a685068f20e5e96e09",
"cross_cats_sorted": [
"cs.AI",
"cs.CV"
],
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"primary_cat": "cs.LG",
"submitted_at": "2022-02-05T21:19:41Z",
"title_canon_sha256": "e21408e4e17327dc419dc0012f7a191d9151a615618959039493008b90e216d2"
},
"schema_version": "1.0",
"source": {
"id": "2202.02643",
"kind": "arxiv",
"version": 1
}
}