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Paper Citation Record · LEDGER

A convergence result of a continuous model of deep learning via a \L{}ojasiewicz--Simon inequality

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2311.15365.

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

pith.paper-citation-record.v1
2311.15365 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:32:23.061524Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T22:12:50.592162Z

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 7f8cfa7e-0fc2-450e-ada2-b8cd73366893 · inbound

Genericity of Polyak-Lojasiewicz Inequalities for Entropic Mean-Field Neural ODEs cites this paper.

Genericity of Polyak-Lojasiewicz Inequalities for Entropic Mean-Field Neural ODEs A convergence result of a continuous model of deep learning via a \L{}ojasiewicz--Simon inequality

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T18:32:23.061524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:32:23.061524Z digest=sha256:76af098128c8ca891ca9b8a9fdb9f13730046204607db7daaad7411ef2e9e6f1

Observation 75db32a4-4c9e-424f-ba93-7ccac4a3da9e · inbound

Man, Machine, and Mathematics cites this paper.

Man, Machine, and Mathematics A convergence result of a continuous model of deep learning via a \L{}ojasiewicz--Simon inequality

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-07-23T00:23:32.673886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-07T09:04:54.618705Z digest=sha256:52f6803bc78178077f8102120ae52bbef66f1a1aebef96b312c2ac49b897ceac

Observation 22dc5385-9850-410e-9700-aa2732980fe8 · inbound

Uniform Scaling Limits in AdamW-Trained Transformers cites this paper.

Uniform Scaling Limits in AdamW-Trained Transformers A convergence result of a continuous model of deep learning via a \L{}ojasiewicz--Simon inequality

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-07-23T00:23:32.673886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T01:24:52.640510Z digest=sha256:7f9711fc474725edf74a457fd634e98998bbf92b2de40376bb423656fcbbf4f5

Observation e96be2c3-4ee3-45a4-95f0-4fc5fdc706b3 · inbound

Training Infinitely Deep and Wide Transformers cites this paper.

Training Infinitely Deep and Wide Transformers A convergence result of a continuous model of deep learning via a \L{}ojasiewicz--Simon inequality

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-07-23T00:23:32.673886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T22:11:55.173003Z digest=sha256:9029d3944e54063bb3c3fc78cfca687fba3c87494200b720bbbf7197b078d373

Observation 314b522b-1abf-42c4-b374-702bc8488dbd · inbound

Dropout and Random Gradient Masking Are Asymptotically Equivalent in Large ResNets cites this paper.

Dropout and Random Gradient Masking Are Asymptotically Equivalent in Large ResNets A convergence result of a continuous model of deep learning via a \L{}ojasiewicz--Simon inequality

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-01T20:07:07.891741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T20:07:07.891741Z digest=sha256:86eec083cdb34bbf794dacc6531826ed3ed507a6318716c2f6dc85ba051dd86c