Pith Number

pith:UBNEOXWT

pith:2017:UBNEOXWT47XHT4FZGBTOFFTNBT

not attested not anchored not stored refs pending

Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network

Adams Wei Yu, Jaime Carbonell, Lei Huang, Qihang Lin, Ruslan Salakhutdinov

arxiv:1707.04822 v2 · 2017-07-16 · cs.LG · cs.AI

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\usepackage{pith}
\pithnumber{UBNEOXWT47XHT4FZGBTOFFTNBT}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ 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

3 papers in Pith

Stochastic convergence of parallel asynchronous adaptive first-order methods

Training Deep Learning Models with Norm-Constrained LMOs

A unified convergence theory for adaptive first-order methods in the nonconvex case, including AdaNorm, full and diagonal AdaGrad, Shampoo and Muo

Receipt and verification

First computed	2026-05-18T00:17:54.656393Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a05a475ed3e7ee79f0b93066e2966d0cc8859a32367d0fbd3218fe2d8f75f055

Aliases

arxiv: 1707.04822 · arxiv_version: 1707.04822v2 · doi: 10.48550/arxiv.1707.04822 · pith_short_12: UBNEOXWT47XH · pith_short_16: UBNEOXWT47XHT4FZ · pith_short_8: UBNEOXWT

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UBNEOXWT47XHT4FZGBTOFFTNBT \
  | 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: a05a475ed3e7ee79f0b93066e2966d0cc8859a32367d0fbd3218fe2d8f75f055

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "538006750f25fd3626b3c1b7fb8e65871aef1c96bafb3d9c391dc0568c9e97af",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2017-07-16T04:47:22Z",
    "title_canon_sha256": "abb761fda133eea2065de944dd2c6d60fd8b505a708a39d2e517c0dbafdcedb0"
  },
  "schema_version": "1.0",
  "source": {
    "id": "1707.04822",
    "kind": "arxiv",
    "version": 2
  }
}