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Paper Citation Record · LEDGER

Gradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2110.08084.

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

pith.paper-citation-record.v1
2110.08084 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 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:35:07.347184Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T14:51:56.738169Z

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 8ba05473-12e8-4ec2-84f1-e29fd8a8382f · inbound

Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime cites this paper.

Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime Gradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-16T10:35:07.347184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:35:07.347184Z digest=sha256:72edcb013deaced86646079dd8e5aa8e512e709b8665c35c547662262e9e2742

Observation 8a7d4edc-e6f8-404c-a320-cb7fa574aecb · inbound

Trajectory inference via Acceleration Matching cites this paper.

Trajectory inference via Acceleration Matching Gradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization

Reference 79

Resolution
metadata mismatch
local_arxiv, observed 2026-08-15T14:51:56.742354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-15T14:51:55.916290Z digest=sha256:0db04efc06017a9d0845b8928fdf03ac65539f392d1653cdbf0d5217bcc60c5a