Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Recurrent neural networks: vanishing and exploding gradients are not the end of the story
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.21064.
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 2026-08-12T10:14:28.246519Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T11:07:15.261338Z
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 0a20bca1-2d29-49e2-8a2e-eb828e3c9728 · inbound
Autocorrelation Matters: Understanding the Role of Initialization Schemes for State Space Models cites this paper.
Autocorrelation Matters: Understanding the Role of Initialization Schemes for State Space Models Recurrent neural networks: vanishing and exploding gradients are not the end of the story
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T10:14:28.246519Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bd71dc8d-1a06-4832-91c6-2db8472c225b · inbound
Revisiting Glorot Initialization for Long-Range Linear Recurrences cites this paper.
Revisiting Glorot Initialization for Long-Range Linear Recurrences Recurrent neural networks: vanishing and exploding gradients are not the end of the story
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:14:10.094309Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f12b30ac-f36e-47b0-aa8d-6b9d6e5d00c4 · inbound
SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks cites this paper.
SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks Recurrent neural networks: vanishing and exploding gradients are not the end of the story
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T11:07:15.262923Z
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.