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 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations 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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T16:44:36.638288Z
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
- 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 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 2026-08-16T06:30:59.297886+00:00.
Observation 16f0848d-8fed-4a6c-a665-7a39131a2566 · inbound
Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models cites this paper.
Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:44:36.638288Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.