Paper Citation Record · LEDGER

Differentiable Particle Filtering without Modifying the Forward Pass

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2106.10314.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2106.10314 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:11:16.498536Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T17:24:57.601900Z

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 bb7805e1-258b-4ef9-93f6-eed6d4a895ed · 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 Differentiable Particle Filtering without Modifying the Forward Pass

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:55.900850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1676c9a-cd4d-4271-a018-62bbe2eca548 · 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 Differentiable Particle Filtering without Modifying the Forward Pass

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T14:11:16.498536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea4fa2a0-9648-45b1-946b-e896b0a3838b · inbound

PyDPF: A Python Package for Differentiable Particle Filtering cites this paper.

PyDPF: A Python Package for Differentiable Particle Filtering Differentiable Particle Filtering without Modifying the Forward Pass

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T07:31:49.112382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64a9eef4-c00e-4340-a8d0-70b02968da27 · inbound

MedBayes-Lite: A Clinical Uncertainty Governance Layer for Risk-Aware Medical Decision Support cites this paper.

MedBayes-Lite: A Clinical Uncertainty Governance Layer for Risk-Aware Medical Decision Support Differentiable Particle Filtering without Modifying the Forward Pass

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T21:09:20.694814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6f0a90b-836e-4487-9f9c-4d473b04bb1f · inbound

Efficient Learning of Deep State Space Models via Importance Smoothing cites this paper.

Efficient Learning of Deep State Space Models via Importance Smoothing Differentiable Particle Filtering without Modifying the Forward Pass

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:19:41.777127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e77998bd-09c5-495d-aa2e-c0ff5d19f46e · inbound

Efficient Learning of Deep State Space Models via Importance Smoothing cites this paper.

Efficient Learning of Deep State Space Models via Importance Smoothing Differentiable Particle Filtering without Modifying the Forward Pass

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:24:57.603222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.