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Paper Citation Record · LEDGER

Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1804.01947.

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

pith.paper-citation-record.v1
1804.01947 v3

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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:49:54.811251Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:41:25.965796Z

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 028464dd-6845-4b47-bdbb-5b4793ed1b23 · inbound

CARoL: Context-aware Adaptation for Robot Learning cites this paper.

CARoL: Context-aware Adaptation for Robot Learning Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T05:49:54.811251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:49:54.811251Z digest=sha256:fad87e9fffb3d9ee6516d75bf1d62cd5f16679f6d04684cc9fa24fd8ce266517

Observation 5f55c3d4-ce08-4194-96e8-2171484c9018 · inbound

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates cites this paper.

Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-06T15:41:26.051804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T15:41:24.995560Z digest=sha256:e34d98851633ff1bc84c21ecec6a3379571e3d708a67785cebe51533efe92687