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Paper Citation Record · LEDGER

Latent Diffusion for Missing Data

As of 21 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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:d1c5a229083d99bcff468d12e66b4d02e45600c0bfc02963aa1b854479099ca1

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:ec8c63d0a057b3b5f7a0f6a775ca576f67c2080ada25ba5d656b6b579d974515

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:573b99f7fc3b11237713c02a8b137ba7c25f1a80257bc1bbcabec6dc92330b63

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:390731568dcffe4fb131d70f3e65d53503f2755e518b610f6ce7c2d0bd6eece4

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:8766d638dfe0f36444ade9419ed0b00a33492456d8f2a79953cd98c9171454ed

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:a0df62565a0329d6489aabd40236e9367da8903f5588b097cda75d1f7c1725ca

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:bf6ed0240fda8eb66dd6fb755a651f062a13bf83e95240ba857d2bcd0234da96

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:ab4d761f68a7dbdfbabc0b519af8d715fa0f23eb7617744a136ea6db4b4d0e29

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:15afae2d97130ebc99108afaba493f0e25ede0a9eaf1a1363ecb94dd2ce28b59

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:1cb882f0d2dcf167045847dcab315dcfff9f3ae831b03700fc8deb54d9db52a2

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:d224ae3f36949ed50b17e7a5f1183890fa525bd9575c7f68d054d35bdbcb7e7d

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.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:eb64a5a506ef1db17b3a2fefd0f67df8859c71a7eaa3e2d2386e252f01a6baf4

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.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:aa96062e23fd8a230ad75c7e952d0b21ceeb577b9be20f4365fa3ffbfe951038

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.

source=pdf_text observed=2026-06-29T14:25:59.908689Z digest=sha256:185e3d93ca6c4b6b5847075c3587ee377a31c1c02b80546d43716e493bb96128

Pith citing papers

No inbound Pith citation observations are available.