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Paper Citation Record · LEDGER

Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2501.03540.

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

pith.paper-citation-record.v1
2501.03540 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:27:25.730496Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T21:48:34.580735Z

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 5c6170b6-822c-40b0-b197-11903325df5c · inbound

Degree-Optimized Cumulative Polynomial Kolmogorov-Arnold Networks cites this paper.

Degree-Optimized Cumulative Polynomial Kolmogorov-Arnold Networks Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T15:27:25.730496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:27:25.730496Z digest=sha256:7502026f63306634b7c3a56c6f8e19b5df3f204e2f3f8364cbfbca72a10c3f8d

Observation 84da070c-6cd1-4d66-ab3c-0c7d3fa0e850 · inbound

Multivariate Uncertainty Quantification with Tomographic Quantile Forests cites this paper.

Multivariate Uncertainty Quantification with Tomographic Quantile Forests Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-16T21:48:34.583121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-16T21:44:02.587459Z digest=sha256:f068a61512ab6deafa39c15aba45d06e0d4fb7ac3be57ae06672cf93665c6f0d