Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2106.09620.
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 2026-08-06T15:56:55.422423Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
21
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation dd8db151-85d0-40e8-ac90-633cd01f57e1 · inbound
Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning cites this paper.
Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:56:55.422423Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ab05fb1e-7ee6-4de2-abc2-e1ca088c60b1 · inbound
Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning cites this paper.
Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-06-27T07:30:40.894192Z
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.