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Paper Citation Record · LEDGER

Semi-Amortized Variational Autoencoders

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1802.02550.

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

pith.paper-citation-record.v1
1802.02550 v7

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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:07:39.792158Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

34
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b1dd2d2d-1d46-4707-8c00-74a67c497a95 · inbound

Mixture Content Selection for Diverse Sequence Generation cites this paper.

Mixture Content Selection for Diverse Sequence Generation Semi-Amortized Variational Autoencoders

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-14T05:07:39.792158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T05:07:39.792158Z digest=sha256:afae32073d12377a9760eca8d3a5f887054e89d70f340df1a3798b40fc33e001

Observation f4022462-4670-4cb7-8c7a-120a877c3e62 · inbound

Case Studies of Generative Machine Learning Models for Dynamical Systems cites this paper.

Case Studies of Generative Machine Learning Models for Dynamical Systems Semi-Amortized Variational Autoencoders

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-08-06T00:01:47.324663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T00:01:46.881304Z digest=sha256:e4db9a0983a71d9cf55e54f26a66a49caa6f1343fcc283a9fdfc200a9b2d172d