Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2209.14863.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T22:45:01.885509Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T17:34:17.241582Z
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 871480ae-d2cd-4978-bf12-d729d99cb580 · inbound
Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression cites this paper.
Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T22:45:01.885509Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b9311f2d-1400-4bb5-8132-e6ce6b82474d · inbound
Sharp convergence rates for Spectral methods via the feature space decomposition method cites this paper.
Sharp convergence rates for Spectral methods via the feature space decomposition method Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T17:34:17.243392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1f566a86-2e0b-47d4-81fe-1a96c2293d48 · inbound
Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine cites this paper.
Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
Reference 39
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-21T07:39:49.391583Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.