Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1606.05320.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T12:33:39.968540Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T12:33:42.809002Z
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 eb645a9e-7789-4d1e-b9a0-f90b3e7bed57 · inbound
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future cites this paper.
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
Reference 181
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T12:33:42.883995Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation c747d0ae-3356-44be-8ec5-315df87265d3 · inbound
Latent States in Neural Networks: Recovering the Temporal Structure of Drifting Data from Model Weights cites this paper.
Latent States in Neural Networks: Recovering the Temporal Structure of Drifting Data from Model Weights Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T07:13:46.476800Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.