Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2103.05744.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-24T06:52:12.821942Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T06:54:03.222914Z
Reference resolution
0 of 0 outbound references displayed
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External citation measurements
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Outbound references
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Pith citing papers
Observation 732bee12-2590-4455-8720-96586c9a40c6 · 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 Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T06:54:03.226439Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.