Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.05068.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:42:58.003414Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T01:12:20.518683Z
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 c6fa3e64-2b6c-4657-a958-5e2a56ccc95d · inbound
Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations cites this paper.
Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems
Reference 46
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T01:12:20.520797Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 713f8007-d2f3-4c14-90a7-caa7310f04ee · inbound
A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks cites this paper.
A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:42:58.003414Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.