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Source: paper_references, paper_reference_links, observed 2026-08-06T11:49:51.325528Z
Paper Citation Record · LEDGER
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
As of 14 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2507.22428.
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51 of 51 outbound references displayed
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Outbound references
Observation 5d80db05-dfb8-414b-907e-210012acf758 · outbound
This paper cites Improving aircraft performance using machine learning: A review.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Improving aircraft performance using machine learning: A review
Reference 1
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This paper cites Deep convolutional neural network based medical image classification for disease diagnosis.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Deep convolutional neural network based medical image classification for disease diagnosis
Reference 2
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This paper cites Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges
Reference 3
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This paper cites Using chatgpt for human–computer interaction research: a primer.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Using chatgpt for human–computer interaction research: a primer
Reference 4
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This paper cites Intriguing properties of neural networks.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Intriguing properties of neural networks
Reference 5
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Observation 48da58a4-5f0e-470c-92c0-40a230bd17e6 · outbound
This paper cites Explaining and harnessing adversarial examples.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Explaining and harnessing adversarial examples
Reference 6
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This paper cites At- tacking vision-based perception in end-to-end autonomous driving models.Journal of Systems Architecture, 110:101766, 2020.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss At- tacking vision-based perception in end-to-end autonomous driving models.Journal of Systems Architecture, 110:101766, 2020
Reference 7
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This paper cites Deepfool: a simple and accurate method to fool deep neural networks.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Deepfool: a simple and accurate method to fool deep neural networks
Reference 8
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This paper cites Carlini and D.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Carlini and D
Reference 9
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Observation 474bd65f-8781-4368-b7db-a0011ea3fa23 · outbound
This paper cites To- wards deep learning models resistant to adversarial attacks.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss To- wards deep learning models resistant to adversarial attacks
Reference 10
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Unresolved cited work
Reference 11
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Observation cac32e72-30f4-4319-949d-7e33c21dbf63 · outbound
This paper cites Generating adversarial examples with adversarial networks.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Generating adversarial examples with adversarial networks
Reference 12
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This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Reference 13
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This paper cites Adversarial training for free! In Advances in Neural Information Processing Systems, volume 32, pages 3358–3369, 2019.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Adversarial training for free! In Advances in Neural Information Processing Systems, volume 32, pages 3358–3369, 2019
Reference 14
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Observation e6635424-dba7-40e8-8b49-6ef1bdc7a35f · outbound
This paper cites Are labels required for improving adversarial robustness? In Advances in Neural Information Processing Systems, volume 32, 2019.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Are labels required for improving adversarial robustness? In Advances in Neural Information Processing Systems, volume 32, 2019
Reference 15
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This paper cites Theoreti- cally principled trade-off between robustness and accuracy.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Theoreti- cally principled trade-off between robustness and accuracy
Reference 16
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This paper cites Boosting adversarial training with hypersphere embedding.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Boosting adversarial training with hypersphere embedding
Reference 17
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This paper cites Improving adver- sarial robustness requires revisiting misclassified examples.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Improving adver- sarial robustness requires revisiting misclassified examples
Reference 18
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This paper cites Adversarial weight perturbation helps robust generalization.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Adversarial weight perturbation helps robust generalization
Reference 19
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This paper cites Do Wider Neural Networks Really Help Adversarial Robustness?.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Do Wider Neural Networks Really Help Adversarial Robustness?
Reference 20
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This paper cites Lafeat: Piercing through adversarial defenses with latent features.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Lafeat: Piercing through adversarial defenses with latent features
Reference 21
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Observation e2af0604-c67b-4cf8-a684-a4bc2f578f72 · outbound
This paper cites Mora: Improving ensemble robustness evaluation with model reweighing attack.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Mora: Improving ensemble robustness evaluation with model reweighing attack
Reference 22
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Lafit: Efficient and reliable evaluation of adversarial defenses with latent features
Reference 23
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This paper cites Composite adversarial attacks.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Composite adversarial attacks
Reference 24
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This paper cites Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size
Reference 25
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Improving Adversarial Robustness of Ensembles with Diversity Training
Reference 26
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Improving adversarial robustness via promoting ensemble diversity
Reference 27
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Reference 28
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Observation da3fd4c9-b731-49ae-b30e-bfbbe2cb24e8 · outbound
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Efficient loss function by minimizing the detrimental effect of floating- point errors on gradient-based attacks
Reference 29
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Adversarial examples in the physical world
Reference 30
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Minimally distorted adversarial examples with a fast adaptive boundary attack
Reference 31
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Gradient-based learning applied to document recognition
Reference 32
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Convolutional deep belief networks on cifar-10
Reference 33
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss RobustBench: a standardized adversarial robustness benchmark
Reference 34
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Guided adversarial attack for evaluating and enhancing adversarial defenses
Reference 35
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Mma training: Direct input space margin maximization through adversarial training
Reference 37
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This paper cites Zico Kolter.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Zico Kolter
Reference 38
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Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Data augmentation can improve robustness
Reference 39
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Observation 034c968e-a32d-4232-af92-f2aec11daddc · outbound
This paper cites Do wider neural networks really help adversarial robustness? In Thirty-Fifth Conference on Neural Information Processing Systems, 2021.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Do wider neural networks really help adversarial robustness? In Thirty-Fifth Conference on Neural Information Processing Systems, 2021
Reference 40
- Resolution
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This paper cites Unlabeled data improves adversarial robustness.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Unlabeled data improves adversarial robustness
Reference 41
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This paper cites HYDRA: Pruning Adversarially Robust Neural Networks.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss HYDRA: Pruning Adversarially Robust Neural Networks
Reference 42
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Observation 4e34ef2b-17d9-44c3-ba72-69e9f8a46549 · outbound
This paper cites Using pre-training can improve model robustness and uncertainty.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Using pre-training can improve model robustness and uncertainty
Reference 43
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Source-reported events for the cited work
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Observation d1baad83-b7ed-4329-9b09-45833aca92e2 · outbound
This paper cites Zico Kolter.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Zico Kolter
Reference 44
- Resolution
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Source-reported events for the cited work
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Observation ed5dbf5a-6c45-416c-97df-f838c1667dce · outbound
This paper cites Self-adaptive training: beyond empirical risk minimiza- tion.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Self-adaptive training: beyond empirical risk minimiza- tion
Reference 45
- Resolution
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Observation a5dc0381-b671-4965-8582-2ead10ca39b2 · outbound
This paper cites Controlling neural level sets.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Controlling neural level sets
Reference 46
- Resolution
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Source-reported events for the cited work
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Observation aa2a8443-6fad-4db0-b7f1-73d81fd09549 · outbound
This paper cites You only propagate once: Accelerating adversarial training via maximal principle.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss You only propagate once: Accelerating adversarial training via maximal principle
Reference 47
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Observation f73d54eb-ae78-4855-af25-7e65819ca216 · outbound
This paper cites SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing
Reference 48
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Observation 9de5ffe7-7485-4d9e-8a26-86e9aaec85f3 · outbound
This paper cites Robustness (python library), 2019.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Robustness (python library), 2019
Reference 49
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 72b22890-c33c-401e-87e4-5f44610dc65f · outbound
This paper cites Do adversarially robust imagenet models transfer better? Advances in Neural Information Processing Systems, 33:3533– 3545, 2020.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Do adversarially robust imagenet models transfer better? Advances in Neural Information Processing Systems, 33:3533– 3545, 2020
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:49:51.488045Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 96efe899-a3ca-4e4f-b718-49b27441f5c3 · outbound
This paper cites Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses.
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T11:49:51.476355Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Pith citing papers
No inbound Pith citation observations are available.