Paper Citation Record · LEDGER

Learning Curves for SGD on Structured Features

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2106.02713.

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

pith.paper-citation-record.v1
2106.02713 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:45:01.729201Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T17:20:00.898403Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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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 59f46074-dd84-4d79-b353-30f07fd49667 · 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 Learning Curves for SGD on Structured Features

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T22:45:01.729201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b7d1605-e725-40b2-b661-06cc2c980cc8 · inbound

Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling cites this paper.

Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling Learning Curves for SGD on Structured Features

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T09:38:49.434717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b9dbe4f-b7ce-4801-8a1d-f7da7a486998 · inbound

Unifying Learning Dynamics and Generalization in Transformers Scaling Law cites this paper.

Unifying Learning Dynamics and Generalization in Transformers Scaling Law Learning Curves for SGD on Structured Features

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T14:02:56.646373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d21ae77e-42af-46f9-bea5-6a0da67e67fc · inbound

Universal One-third Time Scaling in Learning Peaked Distributions cites this paper.

Universal One-third Time Scaling in Learning Peaked Distributions Learning Curves for SGD on Structured Features

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T05:01:10.761729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d100ea2-4755-4cbe-870f-fb928b46f84e · inbound

Neural Scaling Universality: If Exponents Are Fixed, Time to Understand Coefficients cites this paper.

Neural Scaling Universality: If Exponents Are Fixed, Time to Understand Coefficients Learning Curves for SGD on Structured Features

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:20:00.900196Z

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 f41e988c-b174-4943-9c22-ed9ac2c4e491 · inbound

Homogenization of $\ell_2$-Adversarial Training in High-Dimensions: Exact Dynamics under Stochastic Gradient Descent cites this paper.

Homogenization of $\ell_2$-Adversarial Training in High-Dimensions: Exact Dynamics under Stochastic Gradient Descent Learning Curves for SGD on Structured Features

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T17:37:13.853798Z

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 6656265a-06a3-4261-89db-376489d3b9f2 · inbound

A Defense of the Quadratic Model cites this paper.

A Defense of the Quadratic Model Learning Curves for SGD on Structured Features

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-01T06:58:09.845006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.