Pith Number
pith:ECQWZFAQ
pith:2017:ECQWZFAQCLXWONVRPP2AICG42M
not attested
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refs pending
Exploiting random projections and sparsity with random forests and gradient boosting methods -- Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity
arxiv:1704.08067 v1 · 2017-04-26 · stat.ML · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{ECQWZFAQCLXWONVRPP2AICG42M}
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Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
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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 | 2026-05-17T23:45:56.965562Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
20a16c941012ef6736b17bf40408dcd31a7489a52dc71d736ef63ad8fc88991d
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ECQWZFAQCLXWONVRPP2AICG42M \
| 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: 20a16c941012ef6736b17bf40408dcd31a7489a52dc71d736ef63ad8fc88991d
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "8b53127fdf22f3ae563f72c19180c3a9e045329a3df2d1cab35c72851700b6ae",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "stat.ML",
"submitted_at": "2017-04-26T11:45:04Z",
"title_canon_sha256": "8ee53930fcaecbe738b60187555f39a146d88c25db213c797340fc5066b96e49"
},
"schema_version": "1.0",
"source": {
"id": "1704.08067",
"kind": "arxiv",
"version": 1
}
}