Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.11397.
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 2026-08-07T12:59:56.633071Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T10:17:15.691753Z
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 40350839-31f2-42c5-bcff-0eabed68c2f7 · inbound
Improving statistical learning methods via features selection without replacement sampling and random projection cites this paper.
Improving statistical learning methods via features selection without replacement sampling and random projection Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:59:56.633071Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6c3fc50e-a02a-4c9a-af6c-0657310ddd05 · inbound
Rapid training of Hamiltonian graph networks using random features cites this paper.
Rapid training of Hamiltonian graph networks using random features Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T10:17:15.695924Z
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.