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Paper Citation Record · LEDGER

On Efficient Training of Large-Scale Deep Learning Models: A Literature Review

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2304.03589.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2304.03589 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:33:29.788730Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-25T04:45:20.003545Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6711d623-5ac6-4f2d-98df-6ca046d81f3c · inbound

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs cites this paper.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs On Efficient Training of Large-Scale Deep Learning Models: A Literature Review

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-09T21:33:29.788730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:33:29.788730Z digest=sha256:da9f7411e7824513ef62e76e420781d7737b75bfc6af5ccfd01d98fc290e8b7f

Observation 1597a848-3014-4030-b0a8-c9a05d339192 · inbound

MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs cites this paper.

MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs On Efficient Training of Large-Scale Deep Learning Models: A Literature Review

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T09:02:14.400163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-19T09:01:16.991413Z digest=sha256:da7673fd110a9e990d6453b6a2db5a0eaddceb9aebee460c1b8d1c66f0a2fc45

Observation 0182a137-a6b4-4c8f-9758-f4c6795b4bdd · inbound

Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models cites this paper.

Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models On Efficient Training of Large-Scale Deep Learning Models: A Literature Review

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-25T04:45:20.006655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-25T04:44:42.434712Z digest=sha256:8ee3e5b37d246fddfccb6d509c9b2c052ced3899c1e0c40882c7c1166ff5b628