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Paper Citation Record · LEDGER

Training Overparametrized Neural Networks in Sublinear Time

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2208.04508.

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

pith.paper-citation-record.v1
2208.04508 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 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:09:34.897231Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T18:51:14.191407Z

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 470b8d73-10b2-4f94-9fdf-7c7b426ad597 · inbound

Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond cites this paper.

Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond Training Overparametrized Neural Networks in Sublinear Time

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-11T20:09:34.897231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:09:34.897231Z digest=sha256:529d124dff6340e06f4eee74294155f0f9ef8a6b96c8069b04c282fc4a45ec68

Observation 1f7a4694-355c-46d5-a045-2484bd3352cc · inbound

Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolation cites this paper.

Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolation Training Overparametrized Neural Networks in Sublinear Time

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-08-09T18:51:14.197964Z

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=pdf_text observed=2026-08-09T18:51:12.372033Z digest=sha256:dde769d6fd1c4deb39ba81db316c7cdd88d4806f5aaffa096726d53b9d455b0e