Paper Citation Record · LEDGER

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

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 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 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:42:26.508029Z

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 8c908741-882b-4cdb-96e9-335490699f03 · inbound

Low-dimensional adaptation of diffusion models: Convergence in total variation cites this paper.

Low-dimensional adaptation of diffusion models: Convergence in total variation Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T16:42:26.508029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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 2026-08-23T06:30:58.430688+00:00.

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 2026-08-23T06:30:58.430688+00:00.