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Paper Citation Record · LEDGER

Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1705.08841.

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

pith.paper-citation-record.v1
1705.08841 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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:52:05.385459Z

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

0
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 20fd950a-d090-4912-b36d-2af7416e4233 · inbound

Geometric Disentanglement for Generative Latent Shape Models cites this paper.

Geometric Disentanglement for Generative Latent Shape Models Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-14T12:52:05.385459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:52:05.385459Z digest=sha256:ccaf622bb91e03aba18e2d6f108867a3363888e559199e7133b72e772ba7ccb5

Observation 6c21c3de-7814-4536-aa12-97fc0d7eee4f · inbound

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

Case Studies of Generative Machine Learning Models for Dynamical Systems Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

Reference 48

Resolution
verified exact
local_arxiv, observed 2026-08-06T00:01:47.042940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T00:01:46.935949Z digest=sha256:a5180c982b1b94fbf0bad3e9c83211851cdec0470735462f16b73a2e294d784f