Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2501.14458.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:54:58.844812Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T14:34:54.264233Z
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 539db63a-d0cf-46bd-9e58-bf0a381cad6a · inbound
A Physics-Inspired Optimizer: Velocity Regularized Adam cites this paper.
A Physics-Inspired Optimizer: Velocity Regularized Adam A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T14:34:54.269074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation fc2c3735-121c-44aa-80ff-0ef1832a9784 · inbound
DNT: a Deeply Normalized Transformer that can be trained by Momentum SGD cites this paper.
DNT: a Deeply Normalized Transformer that can be trained by Momentum SGD A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:54:58.844812Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.