Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1901.10854.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:09:59.942163Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-14T10:39:29.854276Z
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 1ebe2c1d-4547-40e5-999f-9801a039a1ac · inbound
Space-time error estimates for deep neural network approximations for differential equations cites this paper.
Space-time error estimates for deep neural network approximations for differential equations A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:09:59.942163Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d1075d92-4ded-4507-aea7-264bc3ab3d8e · inbound
Deep neural network approximations for Monte Carlo algorithms cites this paper.
Deep neural network approximations for Monte Carlo algorithms A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T10:39:29.858463Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.