Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SGD Converges to Global Minimum in Deep Learning via Star-convex Path
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1901.00451.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-27T04:33:10.554853Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-03T17:08:43.656388Z
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 23fc618c-3eb2-4735-88f4-68569a925af2 · inbound
Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon cites this paper.
Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon SGD Converges to Global Minimum in Deep Learning via Star-convex Path
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T01:05:16.097192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d8414747-005b-4e89-85b0-5e966fccb4c2 · inbound
On the Convergence Analysis of Muon cites this paper.
On the Convergence Analysis of Muon SGD Converges to Global Minimum in Deep Learning via Star-convex Path
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-05-19T13:22:19.100758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0e0584b0-1d76-4bdb-81f9-1890ccd448a9 · inbound
Stochastic Trust-Region Methods for Over-parameterized Models cites this paper.
Stochastic Trust-Region Methods for Over-parameterized Models SGD Converges to Global Minimum in Deep Learning via Star-convex Path
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T11:51:00.897853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 12a50de9-a4b4-4d02-8195-77d554d981aa · inbound
Stochastic Non-Smooth Convex Optimization with Unbounded Gradients cites this paper.
Stochastic Non-Smooth Convex Optimization with Unbounded Gradients SGD Converges to Global Minimum in Deep Learning via Star-convex Path
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-05-19T15:17:39.232168Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation fa4142f4-1264-4abf-b1dc-27d5133953ce · inbound
Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning cites this paper.
Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning SGD Converges to Global Minimum in Deep Learning via Star-convex Path
Reference 86
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T17:08:43.657832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.