Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2211.15641.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:20:46.654207Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T07:29:38.356564Z
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 3df7c9c6-1abd-410a-bfde-b0a24629b2e4 · inbound
PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning cites this paper.
PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:46.654207Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 48689680-bf34-49ec-88ed-dbc3cbe47fba · inbound
Central limit theorem for the averaged Adam optimizer cites this paper.
Central limit theorem for the averaged Adam optimizer Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T07:29:38.358020Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e7b94612-2d9e-491b-95f3-7b7f410dd143 · inbound
Landscape analysis for shallow neural networks: Complete classification of critical points for cubic activation and affine target functions cites this paper.
Landscape analysis for shallow neural networks: Complete classification of critical points for cubic activation and affine target functions Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T00:02:20.036538Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.