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Paper Citation Record · LEDGER

Semi-Conditional Normalizing Flows for Semi-Supervised Learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1905.00505.

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

pith.paper-citation-record.v1
1905.00505 v4

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:11:14.194788Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:16:02.489423Z

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 553c7ec8-76f5-47a2-bf90-417f712e71fe · inbound

Likelihood Contribution based Multi-scale Architecture for Generative Flows cites this paper.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Semi-Conditional Normalizing Flows for Semi-Supervised Learning

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-14T15:11:14.194788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:11:14.194788Z digest=sha256:39beff8722d93a11f5c7875fe490a5279f0cc8520b22737c93466a73bfb38165

Observation 35d2e243-b557-47ad-a2d2-2a319f30b61d · inbound

Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows cites this paper.

Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows Semi-Conditional Normalizing Flows for Semi-Supervised Learning

Reference 2017

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:16:02.566624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T13:15:59.930197Z digest=sha256:9158bfe22571da4f6dd7edb4e1508a346b5ac0cd6f217c7c10866faf0498b38c