Pith Number
pith:LYRIAA3Q
pith:2021:LYRIAA3QCFK67ZLW6K3MIPOFJR
not attested
not anchored
not stored
refs pending
A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions
arxiv:2108.04620 v1 · 2021-08-10 · math.OC · cs.LG · cs.NA · math.NA
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{LYRIAA3QCFK67ZLW6K3MIPOFJR}
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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.
Cited by
Receipt and verification
| First computed | 2026-07-05T05:28:42.157893Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5e228003701155efe576f2b6c43dc54c7057cd1763da1a6cdc8ca59a3314866c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LYRIAA3QCFK67ZLW6K3MIPOFJR \
| 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: 5e228003701155efe576f2b6c43dc54c7057cd1763da1a6cdc8ca59a3314866c
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "199c1428ac208284d1b973c99c018cd18e9bdb8eeba7f66389cc1840476770ab",
"cross_cats_sorted": [
"cs.LG",
"cs.NA",
"math.NA"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "math.OC",
"submitted_at": "2021-08-10T12:01:37Z",
"title_canon_sha256": "14742a7c8251bfb3986cae67cd9eaf289b4b4dd3810a18e0808f2fe11476a867"
},
"schema_version": "1.0",
"source": {
"id": "2108.04620",
"kind": "arxiv",
"version": 1
}
}