Paper Citation Record · LEDGER

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models

As of 20 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:1908.10744.

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

pith.paper-citation-record.v1
1908.10744 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:50:12.414792Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:28:22.168188Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T13:28:22.546935Z

Reference resolution

36 of 36 outbound references displayed

verified exact2
verified fuzzy27
unresolved7
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 354eca61-10e0-4b44-88ec-5e2407ade09c · outbound

This paper cites Foucart and H.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Foucart and H

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:13.023068Z

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Observation 7847f92a-ab56-4b41-b0d8-6ca781eb3656 · outbound

This paper cites an unresolved cited work.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Unresolved cited work

Reference 2

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Source-reported events for the cited work

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Observation 35bf30d6-b0a2-4c74-9742-0b5ab6e5d98f · outbound

This paper cites Just relax: Convex programming methods for identifying sparse signals in noise,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Just relax: Convex programming methods for identifying sparse signals in noise,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.996183Z

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Observation bd01dac6-9a9a-4315-85fa-3a6951c8bcd5 · outbound

This paper cites Sharp thresholds for high-dimensional and noisy sparsity recovery using 𝓁1-constrained quadratic programming (Lasso),.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Sharp thresholds for high-dimensional and noisy sparsity recovery using 𝓁1-constrained quadratic programming (Lasso),

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.981523Z

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Observation 0d9934d6-7e57-4580-a5c6-c0508158b273 · outbound

This paper cites Greed is good: Algorithmic results for sparse approxi- mation,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Greed is good: Algorithmic results for sparse approxi- mation,

Reference 5

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Observation 222d95cf-4793-46b9-9114-1ab2f9ae6c48 · outbound

This paper cites A sharp condition for exact support recovery with orthogonal matching pursuit,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models A sharp condition for exact support recovery with orthogonal matching pursuit,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.950291Z

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Observation b2f0e28f-0d4b-467e-8350-d02be8adf000 · outbound

This paper cites Information- theoretically optimal compressed sensing via spatial coupling and ap- proximate message passing,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Information- theoretically optimal compressed sensing via spatial coupling and ap- proximate message passing,

Reference 7

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Observation 050a6c85-1882-4084-9cbb-b29435a3dbdb · outbound

This paper cites Living on the edge: Phase transitions in convex programs with random data,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Living on the edge: Phase transitions in convex programs with random data,

Reference 8

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Observation 9d74613d-8066-4b0a-bf7a-d87c43b9efab · outbound

This paper cites Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.904850Z

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Observation c7b6735a-a46b-4a0f-b48b-acaad5b200a9 · outbound

This paper cites On the fundamen- tal limits of adaptive sensing,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models On the fundamen- tal limits of adaptive sensing,

Reference 10

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Observation 8af7e4a3-fcfe-43c0-bf08-c02be6fdb59a · outbound

This paper cites How well can we estimate a sparse vector?.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models How well can we estimate a sparse vector?

Reference 11

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Observation 0fd30918-31fd-433e-b5e9-a94d38382755 · outbound

This paper cites Limits on support recovery with probabilistic models: An information-theoretic framework,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Limits on support recovery with probabilistic models: An information-theoretic framework,

Reference 12

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Observation f5960c67-a90a-42cb-a48b-7421da4118d2 · outbound

This paper cites Foster, Generative Deep Learning : Teaching Machines to Paint, Write, Compose and Play.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Foster, Generative Deep Learning : Teaching Machines to Paint, Write, Compose and Play

Reference 13

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Observation fd3fd69f-4fbf-4c53-8f49-89f428bdf4b7 · outbound

This paper cites Compressed sensing using generative models,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Compressed sensing using generative models,

Reference 14

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Source-reported events for the cited work

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Observation 4b72c8c2-e6f7-43a5-abb1-4fd724dee8e0 · outbound

This paper cites Model-based compressive sensing,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Model-based compressive sensing,

Reference 15

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Source-reported events for the cited work

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Observation 46a12b10-7e79-4145-9877-3c8cce4300e6 · outbound

This paper cites Solving linear inverse problems using GAN priors: An algorithm with provable guarantees,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Solving linear inverse problems using GAN priors: An algorithm with provable guarantees,

Reference 16

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Source-reported events for the cited work

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Observation e35a94a6-f537-4980-bc64-842cbc1fcf5e · outbound

This paper cites Solving inverse problems via auto-encoders.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Solving inverse problems via auto-encoders

Reference 17

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Source-reported events for the cited work

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Observation 038ccc4b-55dd-481f-b168-6f449eff6372 · outbound

This paper cites Modeling sparse deviations for compressed sensing using generative models,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Modeling sparse deviations for compressed sensing using generative models,

Reference 18

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Source-reported events for the cited work

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Observation d50ca8d5-8cf7-4d5d-9824-c799e4262af9 · outbound

This paper cites Global guarantees for enforcing deep generative priors by empirical risk,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Global guarantees for enforcing deep generative priors by empirical risk,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.764147Z

Source-reported events for the cited work

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Observation fb89030d-4d18-46b3-a305-5e1ca6c63e48 · outbound

This paper cites On the statistical rate of nonlinear recovery in generative models with heavy-tailed data,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models On the statistical rate of nonlinear recovery in generative models with heavy-tailed data,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.748472Z

Source-reported events for the cited work

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Observation de2db68e-5298-4e1c-a471-60451ad87613 · outbound

This paper cites Compressed Sensing with Deep Image Prior and Learned Regularization.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Compressed Sensing with Deep Image Prior and Learned Regularization

Reference 21

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Observation 4f060dfe-c8d0-4026-89e8-3945c48bb127 · outbound

This paper cites Deep decoder: Concise image representations from untrained non-convolutional networks,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Deep decoder: Concise image representations from untrained non-convolutional networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.733149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 496ec104-e718-4fb9-912e-73619374cfb0 · outbound

This paper cites Lower Bounds for Compressed Sensing with Generative Models.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Lower Bounds for Compressed Sensing with Generative Models

Reference 23

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Observation 60116c96-3128-4c82-b01a-73de1216e0b4 · outbound

This paper cites Distance-based and continuum Fano inequalities with applications to statistical estimation.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Distance-based and continuum Fano inequalities with applications to statistical estimation

Reference 24

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Observation 6c036ccf-7a94-45d1-b32f-95aca6d2a61b · outbound

This paper cites Information theoretic bounds for compressed sensing,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Information theoretic bounds for compressed sensing,

Reference 25

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Observation e4b9d32f-a0fb-4cda-99ae-9301bd89c7b8 · outbound

This paper cites Shannon-theoretic limits on noisy com- pressive sampling,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Shannon-theoretic limits on noisy com- pressive sampling,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.702151Z

Source-reported events for the cited work

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Observation bc63aad3-c1cc-4e94-a0bf-8f68da75dd38 · outbound

This paper cites Approximate sparsity pattern recovery: Information-theoretic lower bounds,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Approximate sparsity pattern recovery: Information-theoretic lower bounds,

Reference 27

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Source-reported events for the cited work

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Observation fd75cd40-e966-42f7-b78c-7a2221f87d1c · outbound

This paper cites Sparse signal processing with linear and nonlinear observations: A uniﬁed Shannon-theoretic 12 approach,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Sparse signal processing with linear and nonlinear observations: A uniﬁed Shannon-theoretic 12 approach,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.671246Z

Source-reported events for the cited work

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Observation 0431afb5-6bcf-4409-85f0-5ad96494c2f6 · outbound

This paper cites A probabilistic and RIPless theory of compressed sensing,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models A probabilistic and RIPless theory of compressed sensing,

Reference 29

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Observation 191babf0-68cc-435b-b897-8e13b7e1f8a9 · outbound

This paper cites Understanding deep neural networks with rectiﬁed linear units,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Understanding deep neural networks with rectiﬁed linear units,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.639031Z

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Observation 816081eb-5167-4dc5-b6a5-d430bb2e3465 · outbound

This paper cites Representation Benefits of Deep Feedforward Networks.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Representation Benefits of Deep Feedforward Networks

Reference 31

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Source-reported events for the cited work

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Observation ce44426d-1518-40ae-9bca-ad58706274cd · outbound

This paper cites Beneﬁts of depth in neural networks,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Beneﬁts of depth in neural networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.623675Z

Source-reported events for the cited work

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Observation 2e085731-d709-43d1-8191-1b547184e36f · outbound

This paper cites an unresolved cited work.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Unresolved cited work

Reference 33

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Source-reported events for the cited work

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Observation 77ec3b7b-74c5-4e34-930d-f5ef31947efe · outbound

This paper cites An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation

Reference 34

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Observation 2ebc8fea-84c5-4063-a3f3-3b03ed7e76c9 · outbound

This paper cites The universal approximation power of finite-width deep ReLU networks.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models The universal approximation power of finite-width deep ReLU networks

Reference 35

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Observation e409b2b9-8d96-40d0-ade0-d36991e83426 · outbound

This paper cites Depth- width trade-offs for ReLU networks via Sharkovsky’s theorem,.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Depth- width trade-offs for ReLU networks via Sharkovsky’s theorem,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:50:12.588540Z

Source-reported events for the cited work

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Pith citing papers

Observation 73f2f1d2-bd4d-44bc-b438-8c7b0437f5f8 · inbound

Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis cites this paper.

Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.