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Paper Citation Record · LEDGER

Exponential convergence rates for momentum stochastic gradient descent in the overparametrized setting

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2302.03550.

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

pith.paper-citation-record.v1
2302.03550 v2

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T20:46:05.467029Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T20:45:48.869432Z

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 f97c4029-899f-445f-bcd8-352a29f3a66b · inbound

Optimization Hyper-parameter Laws for Large Language Models cites this paper.

Optimization Hyper-parameter Laws for Large Language Models Exponential convergence rates for momentum stochastic gradient descent in the overparametrized setting

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-23T20:45:48.872547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-23T20:45:31.427677Z digest=sha256:676179adee047636f34938a338fd74c7b3a1965e6a47be0cacf1960398c9e701

Observation a4ce39c6-59c4-41a3-865d-09ca359d023c · inbound

Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks cites this paper.

Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks Exponential convergence rates for momentum stochastic gradient descent in the overparametrized setting

Reference 27

Resolution
unresolved
no resolver link, observed 2026-07-11T20:46:05.467029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T20:46:05.467029Z digest=sha256:2757acff2e0952da4d2090cb0d6c868d5ead92dcafc8e945733b34476f684c07