Paper Citation Record · LEDGER

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining

As of 20 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2509.09880.

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

pith.paper-citation-record.v1
2509.09880 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:35:53.600095Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c0c777ee-619c-4a03-9fdc-a19bd195a598 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Generative modeling by estimating gradients of the data distribution,

Reference 1

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Observation 9c878c8f-f84e-457e-88a4-772099b757ca · outbound

This paper cites Denoising diffusion probabilistic models,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Denoising diffusion probabilistic models,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.445684Z

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Observation 4d4151ce-3bf3-45e8-b27e-3ade388ae698 · outbound

This paper cites DiffWave: A versatile diffusion model for audio synthesis,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining DiffWave: A versatile diffusion model for audio synthesis,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.450421Z

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Observation 26d9e908-26ce-4f80-8d9d-4e786e0ae2b6 · outbound

This paper cites Denoising diffusion implicit models,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Denoising diffusion implicit models,

Reference 4

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Observation 7c76ca19-714f-4483-8c5f-1619793e7845 · outbound

This paper cites Video diffusion models,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Video diffusion models,

Reference 5

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Observation 608cc099-429e-4490-bd68-b6dba9fdad7e · outbound

This paper cites Solving inverse problems in medical imaging with score-based generative mod- els,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Solving inverse problems in medical imaging with score-based generative mod- els,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.464528Z

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Observation a3305a82-6fe1-44b4-b6ae-e996eb470dcd · outbound

This paper cites Diffusion posterior sampling for general noisy inverse problems,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Diffusion posterior sampling for general noisy inverse problems,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.469758Z

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Observation ccbc6bf1-5f52-4d8b-9c00-e65fd1b5f867 · outbound

This paper cites Zero-shot image restoration using denoising diffusion null-space model,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Zero-shot image restoration using denoising diffusion null-space model,

Reference 8

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Observation 548acf25-ab06-4597-a549-794f9b9003ce · outbound

This paper cites Pseudoinverse- guided diffusion models for inverse problems,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Pseudoinverse- guided diffusion models for inverse problems,

Reference 9

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Observation c89334a6-fe8f-42bd-9938-e1ff3b96d5d4 · outbound

This paper cites Diffusion models beat GANs on image synthesis,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Diffusion models beat GANs on image synthesis,

Reference 10

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Observation 043c206e-787e-4f46-89c7-c5f22ddc7c29 · outbound

This paper cites Zero-shot adaptation for approximate posterior sampling of diffusion models in inverse problems,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Zero-shot adaptation for approximate posterior sampling of diffusion models in inverse problems,

Reference 11

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Observation d13777a0-004a-4212-ae89-87b9efb78391 · outbound

This paper cites Learning a variational network for reconstruction of accelerated MRI data,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Learning a variational network for reconstruction of accelerated MRI data,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.491790Z

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Observation b8d6d3f5-4242-446e-a895-7f28a486e47a · outbound

This paper cites MoDL: Model- based deep learning architecture for inverse problems,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining MoDL: Model- based deep learning architecture for inverse problems,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.496581Z

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Observation 114458ab-7024-4540-8d4e-6fdbdb25446c · outbound

This paper cites Dense recurrent neural networks for accelerated MRI: history-cognizant unrolling of optimization algorithms,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Dense recurrent neural networks for accelerated MRI: history-cognizant unrolling of optimization algorithms,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.501146Z

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Observation 6a42e9fe-b296-4a06-8036-8d8a4e336ab6 · outbound

This paper cites Zero-shot self-supervised learning for MRI reconstruction,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Zero-shot self-supervised learning for MRI reconstruction,

Reference 15

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Observation 5c4378b6-675d-4ae2-a691-fa021254e412 · outbound

This paper cites Robust compressed sensing MRI with deep generative priors,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Robust compressed sensing MRI with deep generative priors,

Reference 16

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Observation 40fffa12-9232-49c1-a178-aeb53f3bfcd9 · outbound

This paper cites Decomposed diffusion sampler for accelerating large-scale inverse problems,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Decomposed diffusion sampler for accelerating large-scale inverse problems,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.515071Z

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Observation cdb69c6e-844a-4b44-bca1-c13e74bd65d5 · outbound

This paper cites Optimization methods for magnetic resonance image reconstruction,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Optimization methods for magnetic resonance image reconstruction,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.519437Z

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Observation 126cc2d6-d098-4ab2-a8f1-f1eb00e0ff49 · outbound

This paper cites Self-supervised physics-guided deep learning reconstruction for high-resolution 3D LGE CMR,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Self-supervised physics-guided deep learning reconstruction for high-resolution 3D LGE CMR,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.523904Z

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Observation 98dfaa73-81c9-49a4-97ad-0042bf256a61 · outbound

This paper cites Unsuper- vised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing per- spective,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Unsuper- vised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing per- spective,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.528529Z

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Observation 32c1b7c0-8452-4238-817d-6727da89e369 · outbound

This paper cites Fast MRI for all: Bridging equity gaps via training without raw data access,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Fast MRI for all: Bridging equity gaps via training without raw data access,

Reference 21

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Observation abffe061-f39c-4842-b6fe-1fa06383be46 · outbound

This paper cites Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues,

Reference 22

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Observation 45c735e5-5b72-4ce6-92a1-a4de042a995c · outbound

This paper cites Revisitingℓ 1-wavelet compressed-sensing MRI in the era of deep learning,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Revisitingℓ 1-wavelet compressed-sensing MRI in the era of deep learning,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.542112Z

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Observation 4786b679-bbd9-46fe-a20f-3e3243d3695b · outbound

This paper cites Deep learning for accelerated and robust MRI reconstruc- tion,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Deep learning for accelerated and robust MRI reconstruc- tion,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.546854Z

Source-reported events for the cited work

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Observation 4b114251-861a-40be-8bff-c9be62226f5d · outbound

This paper cites Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.551432Z

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Observation 9ffbf23c-bac4-4505-ae83-91d0908c2f5e · outbound

This paper cites Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging,

Reference 26

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Observation 90e6d5df-123d-42c4-954f-7f5a0029340a · outbound

This paper cites Tweedie’s formula and selection bias,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Tweedie’s formula and selection bias,

Reference 27

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Observation a6ca460b-27e5-4938-ba74-83e5b097d48e · outbound

This paper cites Algorithm unrolling: Interpretable, efficient deep learning for signal and image pro- cessing,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Algorithm unrolling: Interpretable, efficient deep learning for signal and image pro- cessing,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.564563Z

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Observation 2b613d13-af97-401f-a39c-3a85a7fe85a0 · outbound

This paper cites 20-fold accelerated 7T fMRI using referenceless self-supervised deep learning reconstruction,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining 20-fold accelerated 7T fMRI using referenceless self-supervised deep learning reconstruction,

Reference 29

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Observation 66dfca4f-b700-4874-921a-45746ba3185b · outbound

This paper cites Physics-driven deep learning for computational magnetic resonance imaging: Combining physics and machine learning for improved medical imaging,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Physics-driven deep learning for computational magnetic resonance imaging: Combining physics and machine learning for improved medical imaging,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.573589Z

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Observation 0616cab6-01cf-45c8-aa16-a4830ee92a76 · outbound

This paper cites A convex compressibility-inspired unsupervised loss function for physics- driven deep learning reconstruction,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining A convex compressibility-inspired unsupervised loss function for physics- driven deep learning reconstruction,

Reference 31

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Observation b8ee82ab-0b2c-4a37-aad5-ce2ffcbb7d1c · outbound

This paper cites Sparsity-driven parallel imaging consistency for improved self-supervised MRI recon- struction,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Sparsity-driven parallel imaging consistency for improved self-supervised MRI recon- struction,

Reference 32

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Observation 3f2f7909-3878-4247-b2cb-813635bd0177 · outbound

This paper cites Sparse MRI: The application of compressed sensing for rapid MR imaging,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Sparse MRI: The application of compressed sensing for rapid MR imaging,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.586898Z

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Observation 35a90fa7-b5f4-4e95-bea7-82a03c75d360 · outbound

This paper cites fastMRI: a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining fastMRI: a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.591243Z

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Observation c106edaf-4dab-4388-9b37-b8926bff26b3 · outbound

This paper cites Assessment of the generalization of learned image reconstruction and the potential for transfer learning,.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Assessment of the generalization of learned image reconstruction and the potential for transfer learning,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.595513Z

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Unavailable: canonical work link unavailable.

Observation 18b8a4c0-fbf8-4d52-90ed-133b096a1728 · outbound

This paper cites Diff-Unfolding: A Model-Based Score Learning Framework for Inverse Problems.

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining Diff-Unfolding: A Model-Based Score Learning Framework for Inverse Problems

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T18:35:53.600095Z

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Pith citing papers

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