Pith Number
pith:P77WZHA7
pith:2020:P77WZHA7O24J5JPMNGDNC2SX6S
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Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
arxiv:2007.06007 v4 · 2020-07-12 · cs.LG · cs.SY · eess.SY · math.OC · stat.ML
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{P77WZHA7O24J5JPMNGDNC2SX6S}
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Record completeness
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4
Citations
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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
Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations
Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations
Receipt and verification
| First computed | 2026-07-05T07:43:06.254602Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
7fff6c9c1f76b89ea5ec6986d16a57f4a1f2ae83ff289a4e762641b8cee322cb
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/P77WZHA7O24J5JPMNGDNC2SX6S \
| 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: 7fff6c9c1f76b89ea5ec6986d16a57f4a1f2ae83ff289a4e762641b8cee322cb
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "0f866744a0baeabe215ea2b50fc08de299c9928f746eb33d4594f490840af57d",
"cross_cats_sorted": [
"cs.SY",
"eess.SY",
"math.OC",
"stat.ML"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2020-07-12T14:53:30Z",
"title_canon_sha256": "15001cccb00947bcf25d0ebe9059a13aa52f252da7edfcf3fdd58e04b19a1d8a"
},
"schema_version": "1.0",
"source": {
"id": "2007.06007",
"kind": "arxiv",
"version": 4
}
}