Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2212.13848.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T12:16:41.849878Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T16:07:08.496739Z
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 ea6d6bdb-0f0f-49ac-83d6-219950fb124a · 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 Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T15:57:07.408393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a3fd0326-4f69-4456-9db0-f9bd547ee99d · 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 Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:07:08.498328Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e40ac79f-6146-4b2a-aa52-997e17d81182 · 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 Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T12:16:41.849878Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.