Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An overview of differentiable particle filters for data-adaptive sequential Bayesian inference
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2302.09639.
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 2026-08-12T14:11:16.501275Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-12T14:09:56.318092Z
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 cc1b4c21-b1e5-4fd1-96b5-dc891dc8a044 · inbound
GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models cites this paper.
GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models An overview of differentiable particle filters for data-adaptive sequential Bayesian inference
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T14:09:56.324479Z
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 55693590-4111-48bf-8f76-8d087f9c5953 · inbound
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks cites this paper.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks An overview of differentiable particle filters for data-adaptive sequential Bayesian inference
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:11:16.501275Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.