Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Analysis of the expected $L_2$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2311.14609.
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 2026-08-02T12:16:41.098670Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T16:07:08.506675Z
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 bc57cc5d-b4a0-44e0-8c50-b89bfa38fa7e · 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 Analysis of the expected $L_2$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:07:08.508210Z
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 9beb56a9-124e-4ed4-bbec-9b7f6f844e6a · 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 Analysis of the expected $L_2$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T12:16:41.098670Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.