Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Generalised multilevel Picard approximations
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1911.03188.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:06:58.756639Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T06:54:03.256033Z
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 9ee605ea-3269-4541-aba7-b5435b5ad0aa · inbound
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense cites this paper.
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Generalised multilevel Picard approximations
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T06:54:03.258335Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d251380b-03fa-4ca6-8a74-027a44a7f623 · inbound
Deep neural networks can provably solve Bellman equations for Markov decision processes without the curse of dimensionality cites this paper.
Deep neural networks can provably solve Bellman equations for Markov decision processes without the curse of dimensionality Generalised multilevel Picard approximations
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:06:58.756639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.