Paper Citation Record · LEDGER

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers

As of 18 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 2 inbound Pith citation observations for arXiv:2504.19000.

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

pith.paper-citation-record.v1
2504.19000 v1

Coverage vector

measured 57 of 57 reference resolution

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T11:17:15.653025Z

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

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Outbound references

Observation e7e62496-65c8-434f-b5f8-1f6af5bc9da8 · outbound

This paper cites On the interpretable adversarial sensitivity of iterative optimizers,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers On the interpretable adversarial sensitivity of iterative optimizers,

Reference 1

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This paper cites Intriguing properties of neural networks.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Intriguing properties of neural networks

Reference 2

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This paper cites Advances in adversarial attacks and defenses in computer vision: A survey,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Advances in adversarial attacks and defenses in computer vision: A survey,

Reference 3

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Observation 931b0589-9b1f-4148-b84a-61b4a1a6b1d8 · outbound

This paper cites Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey

Reference 4

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This paper cites Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

Reference 5

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Observation 25e0baff-e93a-49cc-a1e2-85902151895c · outbound

This paper cites On relating explanations and adversarial examples,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers On relating explanations and adversarial examples,

Reference 6

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This paper cites Understanding adversarial examples from the mutual influence of images and perturbations,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Understanding adversarial examples from the mutual influence of images and perturbations,

Reference 7

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Observation bb833aa7-d3ef-447b-b099-ab5a486bd28d · outbound

This paper cites Adversarial examples are not real fea- tures,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Adversarial examples are not real fea- tures,

Reference 8

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This paper cites Adversarial examples are not bugs, they are features,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Adversarial examples are not bugs, they are features,

Reference 9

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Observation ea1cfdd6-7301-448a-9b16-eb8800e3ef09 · outbound

This paper cites An introduction to convex optimization for communications and signal processing,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers An introduction to convex optimization for communications and signal processing,

Reference 10

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Unresolved cited work

Reference 11

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This paper cites Model-based deep learning: On the intersection of deep learning and optimization,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Model-based deep learning: On the intersection of deep learning and optimization,

Reference 12

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Model-based deep learning,

Reference 13

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This paper cites Learning fast approximations of sparse coding,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Learning fast approximations of sparse coding,

Reference 14

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This paper cites Learning efficient sparse and low rank models,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Learning efficient sparse and low rank models,

Reference 15

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Observation 0d75258a-f561-4715-b209-e3c382fb93b9 · outbound

This paper cites Fista-net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Fista-net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging,

Reference 16

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This paper cites Hyperparameter tuning is all you need for LISTA,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Hyperparameter tuning is all you need for LISTA,

Reference 17

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Ada-LISTA: Learned solvers adaptive to varying models,

Reference 18

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

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,

Reference 19

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

Reference 20

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Unrolled Optimization with Deep Priors

Reference 21

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Learning convex optimization models,

Reference 22

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Learning to optimize: A primer and a benchmark,

Reference 23

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This paper cites Robust optimization–a comprehensive survey,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Robust optimization–a comprehensive survey,

Reference 24

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Distributed optimization and statistical learning via the alternating direction method of multipliers,

Reference 25

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Robust decentralized learning using ADMM with unreliable agents,

Reference 26

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Robust estimation of a location parameter,

Reference 27

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Regression shrinkage and selection via the lasso,

Reference 28

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Recent advances in robust optimization: An overview,

Reference 29

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This paper cites A practical guide to robust optimization,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers A practical guide to robust optimization,

Reference 30

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Efficient algorithms for smooth minimax optimization,

Reference 31

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Assessing optimizer impact on DNN model sensitivity to adversarial examples,

Reference 32

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This paper cites Towards deep learning models resistant to adversarial attacks,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Towards deep learning models resistant to adversarial attacks,

Reference 33

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This paper cites Towards better understanding of training certifiably robust models against adversarial examples,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Towards better understanding of training certifiably robust models against adversarial examples,

Reference 34

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Revisiting and advancing fast adversarial training through the lens of bi-level optimization,

Reference 35

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Optimization and Optimizers for Adversarial Robustness

Reference 36

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This paper cites Adversarial robustness of neural networks from the perspective of Lipschitz calculus: A survey,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Adversarial robustness of neural networks from the perspective of Lipschitz calculus: A survey,

Reference 37

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Observation 7de1af61-b6da-4eb0-9400-9576e8a7e971 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Explaining and Harnessing Adversarial Examples

Reference 38

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Adversarial examples in the physical world,

Reference 39

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Nesterov accelerated gradient and scale invariance for adversarial attacks,

Reference 40

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Towards evaluating the robustness of neural net- works,

Reference 41

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Non-convex optimization for machine learning,

Reference 42

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Proximal algorithms,

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Solving inverse problems with deep neural networks–robustness included?

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.202902Z

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Observation a57bcf7a-62a0-4091-b1f6-ff566632df00 · outbound

This paper cites Learning convex optimization control policies,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Learning convex optimization control policies,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.188662Z

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This paper cites Learn to rapidly and robustly optimize hybrid precoding,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Learn to rapidly and robustly optimize hybrid precoding,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.172925Z

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This paper cites Fifty years of MIMO detection: The road to large-scale MIMOs,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Fifty years of MIMO detection: The road to large-scale MIMOs,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.157351Z

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Observation c99fb7d1-8024-4780-a8a0-7e139cfb2c94 · outbound

This paper cites An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.144889Z

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Robust principal component analysis?

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.124232Z

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Observation 3104f7b0-a8ea-4d0d-b65e-4ff8e27da856 · outbound

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Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Non-convex robust PCA,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.104633Z

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Observation 97cf73ec-8bf6-414d-8cb6-5d5b7a3dec93 · outbound

This paper cites Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.091709Z

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Observation 4a8d2b2e-209f-43ca-a6bb-729cd2613b69 · outbound

This paper cites Deep unrolling for nonconvex robust principal component analysis,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Deep unrolling for nonconvex robust principal component analysis,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.078341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 672b9b3e-c6c7-4500-9498-1cd2e100c48b · outbound

This paper cites Artificial intelligence-empowered hybrid multiple-input/multiple-output beamforming: Learning to optimize for high-throughput scalable MIMO,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Artificial intelligence-empowered hybrid multiple-input/multiple-output beamforming: Learning to optimize for high-throughput scalable MIMO,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.065104Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4295f0b5-80ef-401b-b9c1-710ad65d0ebb · outbound

This paper cites Quadriga: A 3-D multi-cell channel model with time evolution for enabling virtual field trials,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Quadriga: A 3-D multi-cell channel model with time evolution for enabling virtual field trials,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.050119Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1265c4c6-0707-4f25-b776-13edcdd8cdba · outbound

This paper cites Visualizing the loss landscape of neural nets,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Visualizing the loss landscape of neural nets,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.033516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation caad6f74-6c96-4796-83e4-7f9628ba8f47 · outbound

This paper cites an unresolved cited work.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-16T10:09:07.020526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 60b1c9b8-60c3-459f-a127-759d73639c3c · outbound

This paper cites Short proof of a discrete gronwall inequality,.

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Short proof of a discrete gronwall inequality,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:09:07.006895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 578df4c7-460d-4663-978a-0ccc631b7266 · inbound

On Inverse Problems, Parameter Estimation, and Domain Generalization cites this paper.

On Inverse Problems, Parameter Estimation, and Domain Generalization Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:17:15.654760Z

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Observation b5cfc29a-9868-44b5-9c0b-b4a5d40b347d · inbound

Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses cites this paper.

Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T22:22:09.903923Z

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