Paper Citation Record · LEDGER

Latent Diffusion for Missing Data

As of 19 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2605.28427.

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

pith.paper-citation-record.v1
2605.28427 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T14:25:59.908689Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

verified exact3
verified fuzzy0
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch8

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a188d98a-d302-4856-b191-b47573a33c54 · outbound

This paper cites Journal of the American Statistical Association106(496), 1602– 1614 (2011).

Latent Diffusion for Missing Data Journal of the American Statistical Association106(496), 1602– 1614 (2011)

Reference 1

Resolution: metadata mismatch; doi, observed 2026-06-29T14:33:30.324604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6835f28-e04a-4544-8d9b-b1dbc0b5663a · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

Latent Diffusion for Missing Data GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T14:33:30.731910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 60969c10-4f2e-49fa-846b-6c99944aacb0 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Latent Diffusion for Missing Data Denoising Diffusion Probabilistic Models

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-06-29T14:33:30.734536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fc20ce00-f687-4b68-83ba-9c34cc0f58df · outbound

This paper cites MIWAE: Deep Generative Modelling and Imputation of Incomplete Data.

Latent Diffusion for Missing Data MIWAE: Deep Generative Modelling and Imputation of Incomplete Data

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T14:33:30.739342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ace9402f-3c8c-4d90-abe6-5c67ce388acd · outbound

This paper cites MissDiff: Training Diffusion Models on Tabular Data with Missing Values.

Latent Diffusion for Missing Data MissDiff: Training Diffusion Models on Tabular Data with Missing Values

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:33:30.745180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1a3c441e-63c1-471a-9c8e-65d2f77d5f72 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

Latent Diffusion for Missing Data High-Resolution Image Synthesis with Latent Diffusion Models

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T14:33:30.736971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 33a15b2a-ef7e-47c6-b18a-0ecfc134e34c · outbound

This paper cites Improved Techniques for Training GANs.

Latent Diffusion for Missing Data Improved Techniques for Training GANs

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T14:33:30.742146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bf096ea2-9641-4eb6-8c9f-265494497e97 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Latent Diffusion for Missing Data Score-Based Generative Modeling through Stochastic Differential Equations

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T14:33:30.723631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e199a736-a521-4826-be93-b895af303ac3 · outbound

This paper cites GAIN: Missing Data Imputation using Generative Adversarial Nets.

Latent Diffusion for Missing Data GAIN: Missing Data Imputation using Generative Adversarial Nets

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T14:33:30.726461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4d365fd1-84ad-4401-8aea-593defa68401 · outbound

This paper cites DiffPuter: Empowering Diffusion Models for Missing Data Imputation.

Latent Diffusion for Missing Data DiffPuter: Empowering Diffusion Models for Missing Data Imputation

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T14:33:30.729348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 55120c8a-fccb-4256-b193-49f1443ec29e · outbound

This paper cites Diffusion models for missing value imputation in tabular data.

Latent Diffusion for Missing Data Diffusion models for missing value imputation in tabular data

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:33:30.721059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0c909050-21f8-4353-beff-eac95b615c38 · outbound

This paper cites an unresolved cited work.

Latent Diffusion for Missing Data Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T14:25:59.908689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 187c1092-990f-4821-ae0d-77e07da51e08 · outbound

This paper cites an unresolved cited work.

Latent Diffusion for Missing Data Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T14:25:59.908689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f5cf680-d435-4612-b0af-52f321678a31 · outbound

This paper cites IS measures both diversity and clarity of generated images, reflecting how easily they can be classified as distinct digits, with 10 being the highest score.

Latent Diffusion for Missing Data IS measures both diversity and clarity of generated images, reflecting how easily they can be classified as distinct digits, with 10 being the highest score

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T14:25:59.908689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.