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Paper Citation Record · LEDGER

Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2010.02916.

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

pith.paper-citation-record.v1
2010.02916 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T04:48:13.062338Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:16:08.773233Z

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 47cd698e-528b-448b-ab5d-ba04040821ef · inbound

Demystifying Manifold Constraints in LLM Pre-training cites this paper.

Demystifying Manifold Constraints in LLM Pre-training Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:16:08.775984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T17:44:44.438637Z digest=sha256:8dd986cd537b41d66ef4aea014f054b376294b253026caac3d4992f15bc0fa1e

Observation d007b37f-a2bd-4650-be29-dfdb1f092a3d · inbound

Hyperball May Not Be a Free Lunch cites this paper.

Hyperball May Not Be a Free Lunch Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-01T04:48:13.062338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T04:48:13.062338Z digest=sha256:2551bdfd3a2f7e2cd16795e157dfff86cdae14f8cab55f076b7c1b01d0e6bac7