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Paper Citation Record · LEDGER

Conditional Uncertainty Quantification for Tensorized Topological Neural Networks

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.15241.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2410.15241 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:53:49.217375Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-20T07:23:06.986202Z

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 0f813711-fb19-4609-b854-0c26df473dfc · inbound

Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation cites this paper.

Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation Conditional Uncertainty Quantification for Tensorized Topological Neural Networks

Reference 95

Resolution
unresolved
no resolver link, observed 2026-08-08T04:53:49.217375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T04:53:49.217375Z digest=sha256:2bb88bc9ed64cc74ef6c3e04efee1f34289fa59fd79329270a71e6098601c2fb

Observation 557ed671-32be-45f2-a65b-a87c1059edd6 · inbound

Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection cites this paper.

Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection Conditional Uncertainty Quantification for Tensorized Topological Neural Networks

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-20T07:23:06.987617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T07:20:43.847495Z digest=sha256:a09b6821c2c0dca777efd83445cfd0086f27f8ecf97b4f7d7b71b6d6b8f500bc