Paper Citation Record · LEDGER

Variational Approaches for Auto-Encoding Generative Adversarial Networks

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1706.04987.

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

pith.paper-citation-record.v1
1706.04987 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:45:39.418259Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T16:23:04.082994Z

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 7b3959d0-7c24-4ed4-b343-24e4fa00b9da · inbound

Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks cites this paper.

Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks Variational Approaches for Auto-Encoding Generative Adversarial Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T14:45:39.418259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf5c049c-6863-4125-8663-e4684e1a9c71 · inbound

Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image Prior cites this paper.

Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image Prior Variational Approaches for Auto-Encoding Generative Adversarial Networks

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-14T14:31:27.216140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fea0cdc-5336-4599-9a82-62d667c24c55 · inbound

Conditional Flow Variational Autoencoders for Structured Sequence Prediction cites this paper.

Conditional Flow Variational Autoencoders for Structured Sequence Prediction Variational Approaches for Auto-Encoding Generative Adversarial Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T11:26:52.511492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39788da6-ec41-402a-ac8e-f3ddf29658cd · inbound

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space cites this paper.

MoDyGAN: Combining Molecular Dynamics With GANs to Investigate Protein Conformational Space Variational Approaches for Auto-Encoding Generative Adversarial Networks

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:23:04.137274Z

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.