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Paper Citation Record · LEDGER

Neural Topic Model via Optimal Transport

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2008.13537.

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

pith.paper-citation-record.v1
2008.13537 v3

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 2026-08-07T05:43:30.494334Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T10:21:14.847491Z

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 2ee6772e-62b9-4ac2-9dcd-e563c0ea64ee · inbound

Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling cites this paper.

Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling Neural Topic Model via Optimal Transport

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T05:43:30.494334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:43:30.494334Z digest=sha256:9ae5ad4279cf77d961f6371deb7470d2a4177bdb9fbd4e6bd3079e2e5a3a0863

Observation c5dbca5a-f109-4a77-897a-b88d3e27c32d · inbound

LLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability cites this paper.

LLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability Neural Topic Model via Optimal Transport

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-18T10:21:14.849959Z

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=2026-05-18T10:19:23.140392Z digest=sha256:2e66caba4e43dc64748e4bc7aaf0d4d0664a8ad9ced2442af2844473ba9431b2