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Paper Citation Record · LEDGER

Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2309.11420.

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

pith.paper-citation-record.v1
2309.11420 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:35:36.527726Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T05:39:40.849275Z

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 1279af67-3c47-4d21-8d3c-00eb0a553762 · inbound

Sample Complexity and Representation Ability of Test-time Scaling Paradigms cites this paper.

Sample Complexity and Representation Ability of Test-time Scaling Paradigms Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T10:35:36.527726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:35:36.527726Z digest=sha256:7ceaa5a653ebf0e7bdef7d8b3377ae09283ac76398542d6e1dfa9cd5c3b57e11

Observation 918d036d-fd43-4c8f-b507-87348840beac · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models

Reference 266

Resolution
verified exact
arxiv_id, observed 2026-07-04T05:39:40.850965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-26T15:35:51.654392Z digest=sha256:5f309b20e913e0bcafcc0a44604c31e83982d6e4e642c5f10aa6c25d9e7ea2b8

Observation b3dfafed-b261-4c62-b27a-7fed5edd9dde · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models

Reference 266

Resolution
verified exact
arxiv_id, observed 2026-07-02T21:57:25.559828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-07-02T21:51:13.457071Z digest=sha256:a4955e73729984a3f4d71270d7b978f02655c2809dd0824203e9a7443dc92096