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Paper Citation Record · LEDGER

On a continuous time model of gradient descent dynamics and instability in deep learning

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2302.01952.

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

pith.paper-citation-record.v1
2302.01952 v3

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 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T23:34:48.387446Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T13:23:28.005236Z

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 97e88e17-51c1-4b9d-a1c5-d139c23e45ee · inbound

Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization cites this paper.

Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization On a continuous time model of gradient descent dynamics and instability in deep learning

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-02T23:34:48.387446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:34:48.387446Z digest=sha256:59a5b5aa9271d49b55845293bf7df37ce576ffe1d21da08d313e12a5ebd70a4f

Observation b436cf69-ae02-4484-aae7-6fe19d2805bc · inbound

Do Deep Networks Forget Initialization? A Forgetting-Time View of Practical Inductive Bias cites this paper.

Do Deep Networks Forget Initialization? A Forgetting-Time View of Practical Inductive Bias On a continuous time model of gradient descent dynamics and instability in deep learning

Reference 106

Resolution
verified exact
arxiv_id, observed 2026-06-29T13:23:28.007172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-29T13:20:54.303605Z digest=sha256:aa2c1ae2825c6f2e8770a564f43da8359b8154907c4ac68004ff5455394779b5