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Paper Citation Record · LEDGER

Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2007.13640.

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

pith.paper-citation-record.v1
2007.13640 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:10:54.057528Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T03:19:28.901157Z

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 b764848e-861c-4835-b89f-8bd6c4a3629e · inbound

Video Diffusion Models cites this paper.

Video Diffusion Models Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 25

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T14:38:27.990328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T14:38:27.919104Z digest=sha256:1382614857ee49102a4bd789b6bb723755ee78b2915fbc44df989cc582e44773

Observation 1102fb8a-5c80-48c7-b8f6-bb9e89fad139 · inbound

Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding cites this paper.

Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 32

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T07:38:53.449566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T07:38:53.056362Z digest=sha256:171d7525089870f3d0fb701f2e4d9318b49e48947093a844d27de69a4377555b

Observation 81437931-b28f-471a-bfca-4d694f7048e8 · inbound

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions cites this paper.

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:45:22.097255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-11T20:45:21.970404Z digest=sha256:6e29e7d52c3e62ee074b699055bec298c83ae62eeac059cfcd4d7241c5ccff9c

Observation 7e2980ad-c04a-4761-9dc0-bb482ed2fd10 · inbound

A Survey on Diffusion Models for Inverse Problems cites this paper.

A Survey on Diffusion Models for Inverse Problems Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 30

Resolution
metadata mismatch
arxiv_id, observed 2026-05-17T04:31:49.210600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-17T04:31:49.105271Z digest=sha256:3c424257854a9f5552e9c057b152fbee77be5db59d1cadaf71978392d8989a87

Observation 33d8ff94-2bb1-4d2e-8f20-35dcc6821fd4 · inbound

A Mixture-Based Framework for Guiding Diffusion Models cites this paper.

A Mixture-Based Framework for Guiding Diffusion Models Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-09T05:10:54.057528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T05:10:54.057528Z digest=sha256:44af7dccd2012b937ecb6244e4f16ff8525da9538aba1e9d80a74789634c7661

Observation b3caa245-d910-4f00-b8b6-2592ce1fc93f · inbound

Diffusion models under low-noise regime cites this paper.

Diffusion models under low-noise regime Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T05:28:52.094229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:28:52.094229Z digest=sha256:27da0a53a7ebbd0b1868c7caf98bd75bb39a8b5579c166fb4f0c59510a849c5b

Observation b0f9f0ce-6e5a-410f-8938-dfa4f280f67d · inbound

Efficient Zero-Shot Inpainting with Decoupled Diffusion Guidance cites this paper.

Efficient Zero-Shot Inpainting with Decoupled Diffusion Guidance Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-16T20:21:13.525305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T20:19:38.215068Z digest=sha256:e5cc290d5ff24fbb22bf7aa51815e096c17bdd7f6a1465014e49664c98f66476

Observation fb82a2d2-6606-40a3-b9f9-66c03c37417d · inbound

Inverting Data Transformations via Diffusion Sampling cites this paper.

Inverting Data Transformations via Diffusion Sampling Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-03T03:26:41.213511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:26:41.213511Z digest=sha256:b6449f9944eb41daaf6419bf18002dc802a3073ea0d02a558faa4ac15b1dc2d7

Observation 8447dd53-6879-4fb7-86a7-ba1e8c2b48c9 · inbound

A unified perspective on fine-tuning and sampling with diffusion and flow models cites this paper.

A unified perspective on fine-tuning and sampling with diffusion and flow models Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 23

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T15:31:21.632179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-09T19:43:42.331642Z digest=sha256:470ed705988f73c1caa54d0d7949a3cfabeafd3bb80948b65b76dada4802c486

Observation 90a55de6-a131-4dba-9f39-3f99e4e8f5e2 · inbound

Tessellations of Semi-Discrete Flow Matching cites this paper.

Tessellations of Semi-Discrete Flow Matching Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 276

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T03:10:53.770704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-11T02:37:06.288757Z digest=sha256:4e3d2810ee98e51272a9bc3df8521374e7c29435482dd0255f3eee5109ce221d

Observation 7e81ea57-4fd0-4963-b939-bdbc5fbd4f6a · inbound

Beyond MMSE: Enhancing PnP Restoration with ProxiMAP cites this paper.

Beyond MMSE: Enhancing PnP Restoration with ProxiMAP Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 60

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T22:39:10.252130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-20T22:36:46.973024Z digest=sha256:bd53d0f274f1e45a1070eadc2e8ee613e1849b91abf777c858a97249544131f7

Observation 991867d8-971c-4a64-9e73-19cdc6fb6481 · inbound

Memorisation, convergence and generalisation in generative models cites this paper.

Memorisation, convergence and generalisation in generative models Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-21T03:19:28.903217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T03:18:34.227888Z digest=sha256:fb114440dfed92c090199fe0c743cead24e1c592e81c56d1c0d7617c8a94634e

Observation 4aa59b69-ba15-47a1-bf21-f8c6065f112a · inbound

Small, Bias-Free, Blind and Convolutional Denoiser: A compact ConvNeXt U-Net for blind Gaussian color-image denoising cites this paper.

Small, Bias-Free, Blind and Convolutional Denoiser: A compact ConvNeXt U-Net for blind Gaussian color-image denoising Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-01T05:16:02.085868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T05:16:02.085868Z digest=sha256:3b0b9680052cd6fbe978d528087bf53699947888e285199150dac267989ffbd3