Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2303.16535.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-18T17:33:37.594218Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T17:36:41.303327Z
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 1961bd89-b520-48e4-a822-f8d3fb181e44 · inbound
Nonlinear Independent Component Analysis Scheme and its application to gravitational wave data analysis cites this paper.
Nonlinear Independent Component Analysis Scheme and its application to gravitational wave data analysis Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T17:36:41.306147Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4e82bcbf-b66b-481e-95fc-93e152120718 · inbound
PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA cites this paper.
PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T08:39:52.267258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.