Paper Citation Record · LEDGER

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery

As of 21 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2606.12740.

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

pith.paper-citation-record.v1
2606.12740 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T09:44:04.574801Z

measured 43 of 43 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

43 of 43 outbound references displayed

verified exact2
verified fuzzy0
unresolved39
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External citation measurements

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Outbound references

Observation f70d4d3f-ec61-4559-acfb-cff8187f8d4c · outbound

This paper cites Deep Unfolding for Communications Systems: A Survey and Some New Directions.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Deep Unfolding for Communications Systems: A Survey and Some New Directions

Reference 1

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Observation f3f4b746-8caa-4dd0-83c5-f595d254b12e · outbound

This paper cites Baraniuk, Volkan Cevher, Marco F.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Baraniuk, Volkan Cevher, Marco F

Reference 2

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Observation c2062826-a4fd-4663-94c1-2f04abee4bf1 · outbound

This paper cites Real roots of real cubics and optimization.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Real roots of real cubics and optimization

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:58:03.424415Z

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Observation 8d561477-539e-4412-b604-c71bfb9d8c0e · outbound

This paper cites Escaping saddle points without Lipschitz smoothness: The power of nonlinear preconditioning.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Escaping saddle points without Lipschitz smoothness: The power of nonlinear preconditioning

Reference 4

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Observation 078535ad-4040-4704-ac44-5482baef0034 · outbound

This paper cites AMP-Inspired Deep Networks for Sparse Linear Inverse Problems.IEEE Transactions on Signal Processing, 65(16):4293– 4308, 2017.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery AMP-Inspired Deep Networks for Sparse Linear Inverse Problems.IEEE Transactions on Signal Processing, 65(16):4293– 4308, 2017

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation a36442f8-2ada-45ea-8e44-0d333bbcd803 · outbound

This paper cites Carrillo, Kenneth E.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Carrillo, Kenneth E

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 3b19daf3-3e2c-4e92-a497-ba179e5ba214 · outbound

This paper cites Combettes and Christian L.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Combettes and Christian L

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 14bb05ca-3b6c-4840-983a-66c16b76c6ed · outbound

This paper cites Eldar, Patrick Kuppinger, and Helmut Bolcskei.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Eldar, Patrick Kuppinger, and Helmut Bolcskei

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 4d0e509b-cbbc-4862-8573-e0ed5b88f8d6 · outbound

This paper cites Pattern-coupled sparse bayesian learning for recovery of block-sparse signals.IEEE Transactions on Signal Processing, 63(2):360–372, 2015.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Pattern-coupled sparse bayesian learning for recovery of block-sparse signals.IEEE Transactions on Signal Processing, 63(2):360–372, 2015

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation bcf8a188-2440-4965-92f8-2b69617b2d12 · outbound

This paper cites Deep Unfolding Network for Block-Sparse Signal Recovery.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Deep Unfolding Network for Block-Sparse Signal Recovery

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 9d614dc4-43df-4d8b-8b23-5e99a27ca0ad · outbound

This paper cites WEEP: A Differ- entiable Nonconvex Sparse Regularizer via Weakly-Convex Envelope.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery WEEP: A Differ- entiable Nonconvex Sparse Regularizer via Weakly-Convex Envelope

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 40687d39-686d-4828-92fe-20d3aa9b66e7 · outbound

This paper cites Soriaga, and Arash Behboodi.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Soriaga, and Arash Behboodi

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation afc7ce15-ba46-4c47-b365-03441b4b372a · outbound

This paper cites Block-Sparse RPCA for Salient Motion Detection.IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10):1975– 1987, 2014.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Block-Sparse RPCA for Salient Motion Detection.IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10):1975– 1987, 2014

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 9e495c00-6edf-44c9-bf42-e53d8d031007 · outbound

This paper cites What every computer scientist should know about floating-point arith- metic.ACM Comput.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery What every computer scientist should know about floating-point arith- metic.ACM Comput

Reference 14

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Observation ce7c98f2-6881-4c88-9c0a-f8c51fb65b82 · outbound

This paper cites Learning fast approximations of sparse coding.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Learning fast approximations of sparse coding

Reference 15

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Observation 8cd1380e-fadb-4f18-abec-18d8ea04350f · outbound

This paper cites Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

Reference 16

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Observation 382e6686-f004-4c8f-831b-9e1b5540a2e5 · outbound

This paper cites an unresolved cited work.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation fbbe23da-4d53-4455-95fc-f0eec44ebf8f · outbound

This paper cites Trainable ISTA for Sparse Signal Recovery.IEEE Transactions on Signal Processing, 67(12):3113–3125, 2019.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Trainable ISTA for Sparse Signal Recovery.IEEE Transactions on Signal Processing, 67(12):3113–3125, 2019

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 6b4d222d-f075-411d-90cb-ba1ea3a08332 · outbound

This paper cites Differentiable Sparsity via D-Gating: Simple and Versatile Structured Penalization.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Differentiable Sparsity via D-Gating: Simple and Versatile Structured Penalization

Reference 19

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Observation 7d95a1cd-ebb0-452c-be34-3a826f88f77d · outbound

This paper cites M¨ uller, Bernd Bischl, and David R¨ ugamer.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery M¨ uller, Bernd Bischl, and David R¨ ugamer

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 71112508-f833-4967-b277-7138def30d43 · outbound

This paper cites Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 355e54ba-de12-4d67-b387-b78fadecf399 · outbound

This paper cites A convex-nonconvex framework for enhancing minimization induced penal- ties.Journal of the Franklin Institute, 362(15):107969, 2025.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery A convex-nonconvex framework for enhancing minimization induced penal- ties.Journal of the Franklin Institute, 362(15):107969, 2025

Reference 22

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Observation 2a0bfcc7-2030-44dd-b75b-b9e9ba195afd · outbound

This paper cites Theoretical Val- idation of the Latent Optimally Partitioned-L2/L1 Penalty with Application to Angular Power Spectrum Estimation, 2025.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Theoretical Val- idation of the Latent Optimally Partitioned-L2/L1 Penalty with Application to Angular Power Spectrum Estimation, 2025

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

Source-reported events for the cited work

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Observation 4a774870-7571-477b-81d6-91d50bf866e4 · outbound

This paper cites Block-sparse recovery with optimal block partition.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Block-sparse recovery with optimal block partition

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 1975ad38-129f-495b-b08c-effd7243f9b6 · outbound

This paper cites Structured Sparse Cod- ing With the Group Log-regularizer for Key Frame Extraction.IEEE/CAA Journal of Automatica Sinica, 9(10):1818–1830, 2022.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Structured Sparse Cod- ing With the Group Log-regularizer for Key Frame Extraction.IEEE/CAA Journal of Automatica Sinica, 9(10):1818–1830, 2022

Reference 25

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Observation 4fd5def1-0058-49d6-84be-910894b9b4cb · outbound

This paper cites Background Subtraction Based on Low- Rank and Structured Sparse Decomposition.IEEE Transactions on Image Processing, 24(8):2502–2514, 2015.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Background Subtraction Based on Low- Rank and Structured Sparse Decomposition.IEEE Transactions on Image Processing, 24(8):2502–2514, 2015

Reference 26

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Observation 78db3f96-3f28-4197-9e39-f29f085a010a · outbound

This paper cites The Group Lasso for Stable Recovery of Block- Sparse Signal Representations.IEEE Transactions on Signal Processing, 59(4):1371–1382, 2011.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery The Group Lasso for Stable Recovery of Block- Sparse Signal Representations.IEEE Transactions on Signal Processing, 59(4):1371–1382, 2011

Reference 27

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Observation 23407b3c-7c22-40e1-a91a-27f6f74c6d26 · outbound

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Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Unresolved cited work

Reference 28

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Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Unresolved cited work

Reference 29

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Observation 969fe5b1-1578-4fd3-a20c-e6aa9bb4f7c8 · outbound

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Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Unresolved cited work

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 5d98dd51-7525-4d50-a89d-84a06f67bb7a · outbound

This paper cites Deep unfolding: Recent developments, theory, and design guidelines.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Deep unfolding: Recent developments, theory, and design guidelines

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:58:03.427949Z

Source-reported events for the cited work

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Observation cd772589-d43b-4261-bc47-ed62aa5a6dca · outbound

This paper cites On the Reconstruction of Block- Sparse Signals With an Optimal Number of Measurements.IEEE Transactions on Signal Processing, 57(8):3075–3085, 2009.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery On the Reconstruction of Block- Sparse Signals With an Optimal Number of Measurements.IEEE Transactions on Signal Processing, 57(8):3075–3085, 2009

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 0ff6d97a-9439-4c39-a9ff-a7ea18d22b5b · outbound

This paper cites Recovery of sparsely corrupted signals.IEEE Transactions on Information Theory, 58(5):3115–3130, 2012.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Recovery of sparsely corrupted signals.IEEE Transactions on Information Theory, 58(5):3115–3130, 2012

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 194ef443-c0b9-48ad-8641-b6aa9b3d5a41 · outbound

This paper cites Robust Bayesian compressed sensing with outliers.Signal Processing, 140:104–109, 2017.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Robust Bayesian compressed sensing with outliers.Signal Processing, 140:104–109, 2017

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 431923d9-8cf6-4464-b567-d394da93cf78 · outbound

This paper cites Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene.IEEE Transactions on Geoscience and Remote Sensing, 52(9):5736–5750, 2014.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene.IEEE Transactions on Geoscience and Remote Sensing, 52(9):5736–5750, 2014

Reference 35

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Observation 18ae0e71-fd94-40ec-80a5-93cff9ed4f52 · outbound

This paper cites RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection

Reference 36

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Observation 3904b17d-e236-4b02-8833-68bc4392d2e8 · outbound

This paper cites InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5369–5377, 2015.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5369–5377, 2015

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation c283b7be-ac98-471b-b4bd-d77ba2a7f52b · outbound

This paper cites Model selection and estimation in regression with grouped variables.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Model selection and estimation in regression with grouped variables

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 68a3ef2f-a12c-4c01-8418-d8a2bdea078c · outbound

This paper cites Improved analysis of clipping algorithms for non-convex optimization.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Improved analysis of clipping algorithms for non-convex optimization

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 608c484b-eb2a-4318-91c4-367b66f16f5b · outbound

This paper cites ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation f50f6c53-b365-4765-87ff-3b21d815b6c9 · outbound

This paper cites Implicit Regularization in Deep Matrix Factorization.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Implicit Regularization in Deep Matrix Factorization

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T10:58:03.421984Z

Source-reported events for the cited work

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Observation 192d1b1e-117b-4dfc-848c-11d0cd8b1796 · outbound

This paper cites an unresolved cited work.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Unresolved cited work

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Observation 497b5ea4-33fb-4213-acf5-5acd7e605303 · outbound

This paper cites Hyperspectral Anomaly Detection via Structured Sparsity Plus Enhanced Low-Rankness.IEEE Transac- tions on Geoscience and Remote Sensing, 61:1–15, 2023.

Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery Hyperspectral Anomaly Detection via Structured Sparsity Plus Enhanced Low-Rankness.IEEE Transac- tions on Geoscience and Remote Sensing, 61:1–15, 2023

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-27T09:44:04.574801Z

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Pith citing papers

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