Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1903.06652.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:10:00.015724Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T12:39:29.007865Z
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 64c46afe-3e9c-44ac-8824-ea715c91c775 · 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 Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:10:00.015724Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bd7bc629-6375-4edd-a90d-420f76985367 · inbound
Deep neural network approximations for Monte Carlo algorithms cites this paper.
Deep neural network approximations for Monte Carlo algorithms Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:39:29.649643Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 799a36a7-95d9-414f-9d73-742d3058f3d1 · inbound
Deep neural network approximation theory for high-dimensional functions cites this paper.
Deep neural network approximation theory for high-dimensional functions Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
Reference 95
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T12:39:29.011335Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.