Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On Efficient Training of Large-Scale Deep Learning Models: A Literature Review
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2304.03589.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:44:29.709758Z
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 18e5fd35-1719-4844-9b4b-176939978b38 · inbound
UME: Upcycling Mixture-of-Experts for Scalable and Efficient Automatic Speech Recognition cites this paper.
UME: Upcycling Mixture-of-Experts for Scalable and Efficient Automatic Speech Recognition On Efficient Training of Large-Scale Deep Learning Models: A Literature Review
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T05:31:45.774570Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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.
Observation 88b2dda4-33b9-47b6-84ac-2ebec531a6c7 · inbound
ZenFlow: Enabling Stall-Free Offloading Training via Asynchronous Updates cites this paper.
ZenFlow: Enabling Stall-Free Offloading Training via Asynchronous Updates On Efficient Training of Large-Scale Deep Learning Models: A Literature Review
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:29.709758Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-19T06:32:44.657259+00:00.
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 2026-08-19T06:32:44.657259+00:00.