Pith Number
pith:VYCYNUYC
pith:2022:VYCYNUYCZ2QXJTZHH6YG6VUH4R
not attested
not anchored
not stored
refs pending
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning
arxiv:2205.00293 v2 · 2022-04-30 · cs.LG · cs.NE · math.OC
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{VYCYNUYCZ2QXJTZHH6YG6VUH4R}
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 | 2026-07-05T05:01:39.119106Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ae0586d302cea174cf273fb06f5687e473803a23ed8110bb698f81feb24e9005
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/VYCYNUYCZ2QXJTZHH6YG6VUH4R \
| 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: ae0586d302cea174cf273fb06f5687e473803a23ed8110bb698f81feb24e9005
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "448ffe6ddffd91378ae071826b732c0ee5693fcec993ff9c62c7ea94d11a623f",
"cross_cats_sorted": [
"cs.NE",
"math.OC"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.LG",
"submitted_at": "2022-04-30T15:41:13Z",
"title_canon_sha256": "eca8cd9e32e867e7cdeb8e6124db8c0e49e788614063e3158f3b31ca8fa6f1c4"
},
"schema_version": "1.0",
"source": {
"id": "2205.00293",
"kind": "arxiv",
"version": 2
}
}