Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Let data talk: data-regularized operator learning theory for inverse problems
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.09854.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T05:25:32.652354Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T13:02:05.981607Z
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 fc35142d-5c68-4b3e-a0ea-7008fc8a27bb · inbound
Imaging Anisotropic Conductivity from Internal Measurements with Mixed Least-Squares Deep Neural Networks cites this paper.
Imaging Anisotropic Conductivity from Internal Measurements with Mixed Least-Squares Deep Neural Networks Let data talk: data-regularized operator learning theory for inverse problems
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T05:25:32.652354Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18f75e66-21f7-421e-ac63-ab3366e065ad · inbound
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery cites this paper.
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery Let data talk: data-regularized operator learning theory for inverse problems
Reference 2019
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:02:06.089109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.