Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems
As of 23 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T12:16:42.973908Z
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 2026-08-23T06:30:58.430688+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 2026-08-23T06:30:58.430688+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 2026-08-23T06:30:58.430688+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 2026-08-23T06:30:58.430688+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 2026-08-23T06:30:58.430688+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.
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
- 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 2026-08-23T06:30:58.430688+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 2026-08-23T06:30:58.430688+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 2026-08-23T06:30:58.430688+00:00.