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Paper Citation Record · LEDGER

Recent Advances in Optimal Transport for Machine Learning

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.16156.

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

pith.paper-citation-record.v1
2306.16156 v2

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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:02:14.040232Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T15:43:47.933249Z

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 a1479c1d-85b2-44f3-8326-20844de462b6 · inbound

Fused Gromov-Wasserstein Variance Decomposition with Linear Optimal Transport cites this paper.

Fused Gromov-Wasserstein Variance Decomposition with Linear Optimal Transport Recent Advances in Optimal Transport for Machine Learning

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-12T20:02:14.040232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:02:14.040232Z digest=sha256:15a2325c5224dfc68565f535f92f730cf55a1e9a8d385841319270c4a81e76c2

Observation de1d3385-7f45-4308-918b-7edaad2f4d45 · inbound

A dimensionality reduction technique based on the Gromov-Wasserstein distance cites this paper.

A dimensionality reduction technique based on the Gromov-Wasserstein distance Recent Advances in Optimal Transport for Machine Learning

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-10T15:43:47.939944Z

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.

source=pdf_text observed=2026-08-10T15:43:47.816875Z digest=sha256:a26def217069437ea24c9889314e63733f134ea0ba7782bec9ddf50e9c71f72f