Paper Citation Record · LEDGER

Hypothesis Testing in Imaging Inverse Problems

As of 8 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.22481.

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

pith.paper-citation-record.v1
2505.22481 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:15:06.322091Z

measured 47 of 47 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 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

47 of 47 outbound references displayed

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verified fuzzy29
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 88ef83cc-2918-483d-8622-8a7723f69b41 · outbound

This paper cites Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems.

Hypothesis Testing in Imaging Inverse Problems Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:15:07.744881Z

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Observation b5057625-51e1-4dfc-a9db-fa1eec2f9c1e · outbound

This paper cites Angelopoulos and Stephen Bates.

Hypothesis Testing in Imaging Inverse Problems Angelopoulos and Stephen Bates

Reference 2

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Observation 7bd70877-aa4a-4bff-87a5-89a9cbe26024 · outbound

This paper cites Computational imaging.

Hypothesis Testing in Imaging Inverse Problems Computational imaging

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:15.200825Z

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Observation 7a6cfa57-9afb-4dda-a699-e99887610dcf · outbound

This paper cites Uncertainty quantification for radio interferometric imaging: II.

Hypothesis Testing in Imaging Inverse Problems Uncertainty quantification for radio interferometric imaging: II

Reference 4

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Observation 9cd5d790-cbc7-48ef-85e0-d4760315281c · outbound

This paper cites Statistical inference.

Hypothesis Testing in Imaging Inverse Problems Statistical inference

Reference 5

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Observation d8258e76-1bb6-40bd-a5af-8c45871aed77 · outbound

This paper cites Equivariant imaging: Learning beyond the range space.

Hypothesis Testing in Imaging Inverse Problems Equivariant imaging: Learning beyond the range space

Reference 6

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Observation 0cbe3be0-4edc-4ef4-a5a3-282bf95f87ad · outbound

This paper cites Robust equivariant imaging: a fully unsupervised framework for learning to image from noisy and partial measurements.

Hypothesis Testing in Imaging Inverse Problems Robust equivariant imaging: a fully unsupervised framework for learning to image from noisy and partial measurements

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:14.324552Z

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Observation 563c8ab4-9f3d-47a0-b57c-e028ab3a00ba · outbound

This paper cites Imaging with equivariant deep learning: From unrolled network design to fully unsupervised learning.

Hypothesis Testing in Imaging Inverse Problems Imaging with equivariant deep learning: From unrolled network design to fully unsupervised learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:13.982554Z

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Observation b7e138a9-4948-49a9-99f8-c1d6ec84a2de · outbound

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

Hypothesis Testing in Imaging Inverse Problems Diffusion posterior sampling for general noisy inverse problems

Reference 9

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Observation e4f7726d-1b24-405b-8afc-08d1a1278fc3 · outbound

This paper cites Plug-and-play split Gibbs sampler: Embedding deep generative priors in Bayesian inference.

Hypothesis Testing in Imaging Inverse Problems Plug-and-play split Gibbs sampler: Embedding deep generative priors in Bayesian inference

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:13.265216Z

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Observation 5e1c7b10-29df-490e-a536-b24b54b9f338 · outbound

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

Hypothesis Testing in Imaging Inverse Problems A Survey on Diffusion Models for Inverse Problems

Reference 11

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Observation f0163fc2-8c2b-4ccb-a46d-d3983fa1a400 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

Hypothesis Testing in Imaging Inverse Problems Imagenet: A large- scale hierarchical image database

Reference 12

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Observation 6eea2f19-da47-49df-8c4e-e343eee1ad22 · outbound

This paper cites The statistical sign test.

Hypothesis Testing in Imaging Inverse Problems The statistical sign test

Reference 13

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Observation bf27f141-8e90-435d-9c4e-58ecdcf88073 · outbound

This paper cites Efficient Bayesian computation by proximal Markov chain Monte Carlo: When Langevin meets Moreau.

Hypothesis Testing in Imaging Inverse Problems Efficient Bayesian computation by proximal Markov chain Monte Carlo: When Langevin meets Moreau

Reference 14

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Source-reported events for the cited work

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Observation ed06236c-8917-43c6-854d-dec04e99c210 · outbound

This paper cites Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems.

Hypothesis Testing in Imaging Inverse Problems Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:15:07.357697Z

Source-reported events for the cited work

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Observation 4fa3171b-52d5-4a7e-bdc9-4f52f723a00d · outbound

This paper cites Robust hypothesis testing using wasserstein uncertainty sets.

Hypothesis Testing in Imaging Inverse Problems Robust hypothesis testing using wasserstein uncertainty sets

Reference 16

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Observation c92f2ab7-3c93-4e39-91c5-4b46a9ffe204 · outbound

This paper cites Minimax robust hypothesis testing.

Hypothesis Testing in Imaging Inverse Problems Minimax robust hypothesis testing

Reference 17

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Observation fc138ea5-2271-4dc0-9783-69571be57929 · outbound

This paper cites Weinberger.

Hypothesis Testing in Imaging Inverse Problems Weinberger

Reference 18

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Observation 42b5fb9b-74bc-4c93-877d-685a834f1f8e · outbound

This paper cites Deep learning for computational imaging.

Hypothesis Testing in Imaging Inverse Problems Deep learning for computational imaging

Reference 19

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Observation 89d64c59-983f-4806-b305-ab5db488de05 · outbound

This paper cites Bayesian imaging with data-driven priors encoded by neural networks.

Hypothesis Testing in Imaging Inverse Problems Bayesian imaging with data-driven priors encoded by neural networks

Reference 20

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Observation c8600b81-c356-474d-b353-ee5b832a116a · outbound

This paper cites A robust version of the probability ratio test.

Hypothesis Testing in Imaging Inverse Problems A robust version of the probability ratio test

Reference 21

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Observation 4537e426-0afd-45d3-b0f7-581a5ee81810 · outbound

This paper cites Statistical and computational inverse problems, volume 160.

Hypothesis Testing in Imaging Inverse Problems Statistical and computational inverse problems, volume 160

Reference 22

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Observation 3f99b2e3-789d-4c35-ab46-6ee91c45186f · outbound

This paper cites Kamilov, Charles A.

Hypothesis Testing in Imaging Inverse Problems Kamilov, Charles A

Reference 23

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Observation 6d05719b-dfce-4d7e-baa5-b882866cc04f · outbound

This paper cites Bayesian imaging using plug & play priors: When Langevin meets Tweedie.

Hypothesis Testing in Imaging Inverse Problems Bayesian imaging using plug & play priors: When Langevin meets Tweedie

Reference 24

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Observation 58146df1-74e6-446f-86c2-806ade0945a3 · outbound

This paper cites Robust hypothesis testing with a relative entropy tolerance.

Hypothesis Testing in Imaging Inverse Problems Robust hypothesis testing with a relative entropy tolerance

Reference 25

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Observation 15f5f999-3a69-4851-a8b8-c252807c97b2 · outbound

This paper cites Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging.

Hypothesis Testing in Imaging Inverse Problems Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:10.885856Z

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Observation b05af77d-2f4e-417f-97e3-4538e8a9467c · outbound

This paper cites Remoteclip: A vision language foundation model for remote sensing.

Hypothesis Testing in Imaging Inverse Problems Remoteclip: A vision language foundation model for remote sensing

Reference 27

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Observation 9c033b1e-c76d-4cd7-a253-a4c5b1e6e4a9 · outbound

This paper cites Directional statistics.

Hypothesis Testing in Imaging Inverse Problems Directional statistics

Reference 28

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Observation 513d382d-cd14-4a35-aec4-1a6cab839a88 · outbound

This paper cites Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning.

Hypothesis Testing in Imaging Inverse Problems Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning

Reference 29

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Observation e24fe117-cc85-4f38-919c-b3146089bcad · outbound

This paper cites Revisiting the calibration of modern neural networks.

Hypothesis Testing in Imaging Inverse Problems Revisiting the calibration of modern neural networks

Reference 30

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Observation c7099f29-0136-46fc-880a-3719d459b85d · outbound

This paper cites Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian Noise.

Hypothesis Testing in Imaging Inverse Problems Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian Noise

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:15:07.026108Z

Source-reported events for the cited work

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Observation 211f9bf9-7d6f-4499-89d0-5f17448fed66 · outbound

This paper cites Learned reconstruction methods with convergence guarantees: A survey of concepts and applications.

Hypothesis Testing in Imaging Inverse Problems Learned reconstruction methods with convergence guarantees: A survey of concepts and applications

Reference 32

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Observation 6fe8a59f-7af7-4705-a980-5ead441363df · outbound

This paper cites Automated flower classification over a large number of classes.

Hypothesis Testing in Imaging Inverse Problems Automated flower classification over a large number of classes

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:09.952162Z

Source-reported events for the cited work

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Observation 17a6b4a2-467e-40cf-9432-ed76d79427d2 · outbound

This paper cites Know "no” better: A data-driven approach for enhancing negation awareness in CLIP.

Hypothesis Testing in Imaging Inverse Problems Know "no” better: A data-driven approach for enhancing negation awareness in CLIP

Reference 34

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Observation 19974b1c-9c31-4614-8d00-7a970ce3ca00 · outbound

This paper cites Maximum-a-Posteriori estimation with Bayesian confidence regions.

Hypothesis Testing in Imaging Inverse Problems Maximum-a-Posteriori estimation with Bayesian confidence regions

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:09.724198Z

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

Observation 0261bd03-a674-45c9-90fb-520c30ace514 · outbound

This paper cites Equivariant bootstrapping for uncertainty quantification in imaging inverse problems.

Hypothesis Testing in Imaging Inverse Problems Equivariant bootstrapping for uncertainty quantification in imaging inverse problems

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:09.542787Z

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Observation d0dece15-5c08-487b-a686-538d9303679d · outbound

This paper cites Hero, and Steve McLaughlin.

Hypothesis Testing in Imaging Inverse Problems Hero, and Steve McLaughlin

Reference 37

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Observation 35b23504-14a1-487f-85ba-9ca20bd5e6c1 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Hypothesis Testing in Imaging Inverse Problems Learning transferable visual models from natural language supervision

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:09.267684Z

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Observation ab375dfe-3e6f-4442-896d-9c9d11813874 · outbound

This paper cites Hypothesis testing with e-values.

Hypothesis Testing in Imaging Inverse Problems Hypothesis testing with e-values

Reference 39

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Observation 6729aadc-f92c-4e83-b172-1edf2583dc85 · outbound

This paper cites Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimization.

Hypothesis Testing in Imaging Inverse Problems Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:09.009723Z

Source-reported events for the cited work

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Observation 0fb7f54f-698e-455c-89c3-54c1bd48414a · outbound

This paper cites LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization.

Hypothesis Testing in Imaging Inverse Problems LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization

Reference 41

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Source-reported events for the cited work

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Observation d7dc8ffe-bac9-4192-85f3-0d616ba83cb4 · outbound

This paper cites Deepinverse: A deep learning framework for inverse problems in imaging.

Hypothesis Testing in Imaging Inverse Problems Deepinverse: A deep learning framework for inverse problems in imaging

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:08.678821Z

Source-reported events for the cited work

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Observation 3a3b1620-ca25-4351-b711-c386f993473f · outbound

This paper cites Reconstruct any- thing model: a lightweight foundation model for computational imaging.

Hypothesis Testing in Imaging Inverse Problems Reconstruct any- thing model: a lightweight foundation model for computational imaging

Reference 43

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Source-reported events for the cited work

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Observation 43c95666-1eeb-4095-8150-040b2835c8bc · outbound

This paper cites Do Bayesian imaging methods report trustworthy probabilities?.

Hypothesis Testing in Imaging Inverse Problems Do Bayesian imaging methods report trustworthy probabilities?

Reference 44

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Source-reported events for the cited work

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Observation be447537-dffb-4dbc-a47d-921d62bac0c0 · outbound

This paper cites MedCLIP: Contrastive learning from unpaired medical images and text.

Hypothesis Testing in Imaging Inverse Problems MedCLIP: Contrastive learning from unpaired medical images and text

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:08.525307Z

Source-reported events for the cited work

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Observation 53d0caa1-5165-4473-9793-c453c314de73 · outbound

This paper cites All of nonparametric statistics.

Hypothesis Testing in Imaging Inverse Problems All of nonparametric statistics

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:08.223998Z

Source-reported events for the cited work

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Observation b3584fc0-b0ad-4bff-8e00-d2dc2c948577 · outbound

This paper cites limited risk.

Hypothesis Testing in Imaging Inverse Problems limited risk

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:07.959090Z

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.

Pith citing papers

No inbound Pith citation observations are available.