Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Optimal rates of approximation by shallow ReLU$^k$ neural networks and applications to nonparametric regression
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2304.01561.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T15:14:01.611692Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-15T15:14:01.653015Z
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 d3c705b8-5ec8-4e97-a307-cb4816e33f0b · inbound
Do Neural Networks Really Beat the Curse of Dimensionality? A Bit-Complexity View cites this paper.
Do Neural Networks Really Beat the Curse of Dimensionality? A Bit-Complexity View Optimal rates of approximation by shallow ReLU$^k$ neural networks and applications to nonparametric regression
Reference 80
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T15:14:01.659841Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.