Pith Number
pith:ZI47YWAP
pith:2018:ZI47YWAPZ67WZDQ4KGRNC5WP7H
not attested
not anchored
not stored
refs pending
How can deep learning advance computational modeling of sensory information processing?
arxiv:1810.08651 v1 · 2018-09-25 · cs.NE · cs.LG · stat.ML
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{ZI47YWAPZ67WZDQ4KGRNC5WP7H}
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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 | 2026-05-18T00:02:43.904851Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ca39fc580fcfbf6c8e1c51a2d176cff9e1e4568b407dc72ea2d1f0d45545826f
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZI47YWAPZ67WZDQ4KGRNC5WP7H \
| 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: ca39fc580fcfbf6c8e1c51a2d176cff9e1e4568b407dc72ea2d1f0d45545826f
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "66cfb8265511162605e67ae3fcb293b0d890b4981c8839d4aeea02977113d9a0",
"cross_cats_sorted": [
"cs.LG",
"stat.ML"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.NE",
"submitted_at": "2018-09-25T23:39:34Z",
"title_canon_sha256": "b1abe2f889a235b5b21d65fd5b8a44ed8a73761fcdc1a748949ed9571307679f"
},
"schema_version": "1.0",
"source": {
"id": "1810.08651",
"kind": "arxiv",
"version": 1
}
}