Paper Citation Record · LEDGER

Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1905.09870.

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

pith.paper-citation-record.v1
1905.09870 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T12:16:42.973908Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T14:49:54.902609Z

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 e56068ef-b400-4a52-a3cc-95650784d892 · inbound

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models cites this paper.

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems

Reference 96

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T23:00:21.330637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6dba53df-2264-4032-b059-820247e2d77d · inbound

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks cites this paper.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:37:41.914956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9246c17a-fb09-40af-9fd1-c520baa2e571 · inbound

Population Risk Bounds for Kolmogorov-Arnold Networks Trained by DP-SGD with Correlated Noise cites this paper.

Population Risk Bounds for Kolmogorov-Arnold Networks Trained by DP-SGD with Correlated Noise Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:43:00.870563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 18b63de0-e0ae-4570-80c5-1575137f9f83 · inbound

Optimal Rates for Generalization of Gradient Descent Methods with Deep Neural Networks cites this paper.

Optimal Rates for Generalization of Gradient Descent Methods with Deep Neural Networks Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:57:07.396985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f76ee476-e345-4afd-9aa2-40c6a07aa2f6 · inbound

Minimax-Optimal Generalization Bounds for Smooth Deep Neural Networks Trained by (Stochastic) Gradient Descent cites this paper.

Minimax-Optimal Generalization Bounds for Smooth Deep Neural Networks Trained by (Stochastic) Gradient Descent Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:07:08.510992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f14d8b6b-c8eb-44e2-8ba6-cfc0d304c6f9 · inbound

Minimax-Optimal Generalization Bounds for Smooth Deep Neural Networks Trained by (Stochastic) Gradient Descent cites this paper.

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-02T12:16:42.973908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ed481b6-32df-442c-989b-7e1e45308d7d · inbound

A Theory on Flow Matching with Neural Networks cites this paper.

A Theory on Flow Matching with Neural Networks Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems

Reference 123

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:57:29.386764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 15290d15-d8d8-4826-87ad-075ba7be7053 · inbound

Structure Before Collapse: Transient semantic geometry in next-token prediction cites this paper.

Structure Before Collapse: Transient semantic geometry in next-token prediction Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems

Reference 272

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T13:29:51.148754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 74ac9a67-721b-41c0-9fea-69195c08c7f4 · inbound

Estimation of High Dimensional Bounded Discrete Graphical Models via Regularized Generalized Score Matching cites this paper.

Estimation of High Dimensional Bounded Discrete Graphical Models via Regularized Generalized Score Matching Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems

Reference 256

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T14:49:54.906031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.