Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Operator learning with PCA-Net: upper and lower complexity bounds
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2303.16317.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:19:41.578374Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T00:47:30.228379Z
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 7e5a229c-c081-4776-af03-aad85d120650 · inbound
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning cites this paper.
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning Operator learning with PCA-Net: upper and lower complexity bounds
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:19:41.578374Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e880704e-54e8-42d4-9593-ffc291f645d4 · inbound
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models cites this paper.
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models Operator learning with PCA-Net: upper and lower complexity bounds
Reference 67
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T00:47:30.230055Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ebafb86f-d617-41ef-8eb0-bf7b8808d2b4 · inbound
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models cites this paper.
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models Operator learning with PCA-Net: upper and lower complexity bounds
Reference 67
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T12:05:40.376215Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.