Paper Citation Record · LEDGER

Deep Distillation Gradient Preconditioning for Inverse Problems

As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2508.04832.

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

pith.paper-citation-record.v1
2508.04832 v1

Coverage vector

measured 40 of 40 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-05-21T23:14:20.370343Z

measured 40 of 40 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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verified fuzzy34
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External citation measurements

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Outbound references

Observation 4a647f7f-c000-42d3-8b86-95ef20ad2c81 · outbound

This paper cites Image super-resolution via sparse representation.

Deep Distillation Gradient Preconditioning for Inverse Problems Image super-resolution via sparse representation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.301535Z

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Observation bf33921b-2b16-44ef-b647-d48fab5976f4 · outbound

This paper cites Coil sensitivity encoding for fast mri.

Deep Distillation Gradient Preconditioning for Inverse Problems Coil sensitivity encoding for fast mri

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.311977Z

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Observation 134bf0c8-4f4c-4255-acd3-f96cd8a51708 · outbound

This paper cites Single-pixel imaging via compressive sampling.

Deep Distillation Gradient Preconditioning for Inverse Problems Single-pixel imaging via compressive sampling

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.306566Z

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Observation c644ca5d-d04d-4def-857f-d7315b895b03 · outbound

This paper cites Improving compressive imaging recovery via measurement augmentation.

Deep Distillation Gradient Preconditioning for Inverse Problems Improving compressive imaging recovery via measurement augmentation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.294348Z

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Observation 7b75469b-5646-452b-a749-5bc76cbd4180 · outbound

This paper cites Matrix conditioning and nonlinear optimization.

Deep Distillation Gradient Preconditioning for Inverse Problems Matrix conditioning and nonlinear optimization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.328072Z

Source-reported events for the cited work

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Observation 5fbea26a-1bca-442f-9c5a-5a9c5166d04f · outbound

This paper cites Least squares optimization with l1-norm regularization.

Deep Distillation Gradient Preconditioning for Inverse Problems Least squares optimization with l1-norm regularization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.309120Z

Source-reported events for the cited work

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Observation 7144b5fc-3066-4992-8321-6096ab2d1ef8 · outbound

This paper cites Tikhonov regularization and total least squares.

Deep Distillation Gradient Preconditioning for Inverse Problems Tikhonov regularization and total least squares

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.281992Z

Source-reported events for the cited work

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Observation 7883b65c-7176-4dec-b415-2eee37d5faaf · outbound

This paper cites Edge-preserving and scale-dependent properties of total variation regularization.

Deep Distillation Gradient Preconditioning for Inverse Problems Edge-preserving and scale-dependent properties of total variation regularization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.275066Z

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Observation 377a2a2e-5dba-49a9-ad8c-a669cb942357 · outbound

This paper cites A fast iterative shrinkage-thresholding algorithm for linear inverse problems.

Deep Distillation Gradient Preconditioning for Inverse Problems A fast iterative shrinkage-thresholding algorithm for linear inverse problems

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.314931Z

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Observation b0fec247-e385-4207-90f4-ed53578cda64 · outbound

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Deep Distillation Gradient Preconditioning for Inverse Problems Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-21T23:14:26.288177Z

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Observation 859853d1-9e2c-47f7-8ae5-c272d6635f32 · outbound

This paper cites Plug-and-play priors for model based reconstruction.

Deep Distillation Gradient Preconditioning for Inverse Problems Plug-and-play priors for model based reconstruction

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.335485Z

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.

Observation e5e8c2c6-b68b-4943-8377-09019dc652cd · outbound

This paper cites The little engine that could: Regularization by denoising (red).

Deep Distillation Gradient Preconditioning for Inverse Problems The little engine that could: Regularization by denoising (red)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.260063Z

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Observation b5b53d91-64fd-472e-b59f-2020c11c3fcf · outbound

This paper cites Deep learned non-linear propagation model regularizer for compressive spectral imaging.

Deep Distillation Gradient Preconditioning for Inverse Problems Deep learned non-linear propagation model regularizer for compressive spectral imaging

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.320587Z

Source-reported events for the cited work

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Observation 4fcdf756-6e7f-4556-9f3c-1d62b4dfa4c1 · outbound

This paper cites Efficient preconditioners for optimality systems arising in connection with inverse problems.

Deep Distillation Gradient Preconditioning for Inverse Problems Efficient preconditioners for optimality systems arising in connection with inverse problems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.333037Z

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Observation 65e1ad7d-c983-4521-919b-649436147d29 · outbound

This paper cites A precondi- tioner for a primal-dual newton conjugate gradient method for compressed sensing problems.

Deep Distillation Gradient Preconditioning for Inverse Problems A precondi- tioner for a primal-dual newton conjugate gradient method for compressed sensing problems

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.330219Z

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

Observation 1e705064-339c-444d-be68-56c0e557c053 · outbound

This paper cites Conjugate-gradient preconditioning methods for shift-variant pet image reconstruction.

Deep Distillation Gradient Preconditioning for Inverse Problems Conjugate-gradient preconditioning methods for shift-variant pet image reconstruction

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.325831Z

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.

Observation 0930d8f8-6b15-4b8c-8233-73e622f3fda6 · outbound

This paper cites Polynomial preconditioners for regularized linear inverse problems.

Deep Distillation Gradient Preconditioning for Inverse Problems Polynomial preconditioners for regularized linear inverse problems

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.291516Z

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.

Observation a963b4bf-fdcc-4724-8216-19dce0374a0c · outbound

This paper cites On the origins of linear and non-linear preconditioning.

Deep Distillation Gradient Preconditioning for Inverse Problems On the origins of linear and non-linear preconditioning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.274730Z

Source-reported events for the cited work

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Observation 0b4778dd-269f-47b1-bfbf-329d3de6bd64 · outbound

This paper cites Learning preconditioners for inverse problems.

Deep Distillation Gradient Preconditioning for Inverse Problems Learning preconditioners for inverse problems

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.317781Z

Source-reported events for the cited work

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Observation e4c464e0-d381-442c-88a9-3b96d95bf13e · outbound

This paper cites Distilling the knowledge in a neural network.

Deep Distillation Gradient Preconditioning for Inverse Problems Distilling the knowledge in a neural network

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.271008Z

Source-reported events for the cited work

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Observation 5a6d959b-15a5-44ec-930b-f1c44dfb9e8b · outbound

This paper cites Distilling Knowledge for Designing Computational Imaging Systems.

Deep Distillation Gradient Preconditioning for Inverse Problems Distilling Knowledge for Designing Computational Imaging Systems

Reference 21

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Source-reported events for the cited work

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Observation 466bac3b-1c09-4a5d-823a-42cf5837e404 · outbound

This paper cites Hadamard single-pixel imaging versus fourier single-pixel imaging.

Deep Distillation Gradient Preconditioning for Inverse Problems Hadamard single-pixel imaging versus fourier single-pixel imaging

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.252051Z

Source-reported events for the cited work

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Observation a6f4cb3a-099e-4ec8-8376-f23374c85eed · outbound

This paper cites An overview of bilevel optimization.

Deep Distillation Gradient Preconditioning for Inverse Problems An overview of bilevel optimization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.299004Z

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Observation 1f175e95-325a-451a-9fba-0affd5ebbc26 · outbound

This paper cites Adam: A method for stochastic optimization.

Deep Distillation Gradient Preconditioning for Inverse Problems Adam: A method for stochastic optimization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.278306Z

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Observation 3f91aced-a4ac-40f5-9038-d4900c2bba54 · outbound

This paper cites Decoupled Weight Decay Regularization.

Deep Distillation Gradient Preconditioning for Inverse Problems Decoupled Weight Decay Regularization

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:14:25.683609Z

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.

Observation c645b9cd-ea30-4e5b-9059-eb760e014dde · outbound

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

Deep Distillation Gradient Preconditioning for Inverse Problems DeepInverse: A deep learning framework for inverse problems in imaging

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.304084Z

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.

Observation 7e88aaf1-1d2e-4cbe-9cfd-312b7a53a022 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].

Deep Distillation Gradient Preconditioning for Inverse Problems The mnist database of handwritten digit images for machine learning research [best of the web]

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.297050Z

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

Observation 12f9c9bc-0d63-48af-815c-7e315b82e52f · 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.

Deep Distillation Gradient Preconditioning for Inverse Problems FastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.343848Z

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Observation 849bda89-f6d1-47a9-aac1-3b7e6a3d842b · outbound

This paper cites Deep learning face attributes in the wild.

Deep Distillation Gradient Preconditioning for Inverse Problems Deep learning face attributes in the wild

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.285114Z

Source-reported events for the cited work

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Observation c43369fe-882d-412a-abab-f40fa8209bbc · outbound

This paper cites The perceptron: a probabilistic model for information storage and organization in the brain.

Deep Distillation Gradient Preconditioning for Inverse Problems The perceptron: a probabilistic model for information storage and organization in the brain

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.271388Z

Source-reported events for the cited work

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Observation 3a8647c8-ee27-4c5b-ad66-8590c1a9cd2e · outbound

This paper cites Gradient-based learning applied to document recognition.

Deep Distillation Gradient Preconditioning for Inverse Problems Gradient-based learning applied to document recognition

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.340766Z

Source-reported events for the cited work

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Observation 494df721-7acf-4ab0-84da-6283b84777ea · outbound

This paper cites Cbam: Convolutional block attention module.

Deep Distillation Gradient Preconditioning for Inverse Problems Cbam: Convolutional block attention module

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.338125Z

Source-reported events for the cited work

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Observation d932281a-ee5e-4ade-bf95-459c98a82bfc · outbound

This paper cites U-net: Con- volutional networks for biomedical image segmentation.

Deep Distillation Gradient Preconditioning for Inverse Problems U-net: Con- volutional networks for biomedical image segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.291280Z

Source-reported events for the cited work

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Observation 5e752816-d14b-4c58-9c97-049356023ebf · outbound

This paper cites A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening.

Deep Distillation Gradient Preconditioning for Inverse Problems A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.263571Z

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

Observation dba3c966-6cc0-47a7-8502-1b331417e5dc · outbound

This paper cites Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration.

Deep Distillation Gradient Preconditioning for Inverse Problems Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:14:25.669155Z

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Observation c68f5e4e-da3b-4b42-8d27-56a9de4e55bd · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Deep Distillation Gradient Preconditioning for Inverse Problems An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-21T23:14:25.663987Z

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.

Observation 40b74a34-9a89-4953-9f53-0fda8c43e64d · outbound

This paper cites A convnet for the 2020s.

Deep Distillation Gradient Preconditioning for Inverse Problems A convnet for the 2020s

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.323240Z

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

Observation 259e63b2-d9cd-4492-b252-31d899c6b83e · outbound

This paper cites Schedul- ing techniques for liver segmentation: Reducelronplateau vs onecyclelr.

Deep Distillation Gradient Preconditioning for Inverse Problems Schedul- ing techniques for liver segmentation: Reducelronplateau vs onecyclelr

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.308652Z

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

Observation 4674c066-b02a-4a36-a70f-86ccb3424cce · outbound

This paper cites On the expressive power of deep neural networks.

Deep Distillation Gradient Preconditioning for Inverse Problems On the expressive power of deep neural networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T23:14:26.305878Z

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

Observation b833f4ce-fc91-455e-b457-edf55677df4d · outbound

This paper cites an unresolved cited work.

Deep Distillation Gradient Preconditioning for Inverse Problems Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-05-21T23:14:26.303061Z

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.

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