Paper Citation Record · LEDGER

Intriguing Properties of Robust Classification

As of 19 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2412.04245.

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

pith.paper-citation-record.v1
2412.04245 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:46:08.595916Z

measured 49 of 49 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 2026-08-02T14:57:31.353642Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

verified exact3
verified fuzzy39
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 987904d2-df95-48ce-b575-dd0593649624 · outbound

This paper cites Raising the Bar for Certified Adversarial Robustness with Diffusion Models.

Intriguing Properties of Robust Classification Raising the Bar for Certified Adversarial Robustness with Diffusion Models

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-11T21:46:08.690109Z

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 292b5ca9-4f86-4c64-b468-5198bcccb9f4 · outbound

This paper cites Sorting out Lip- schitz function approximation.

Intriguing Properties of Robust Classification Sorting out Lip- schitz function approximation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.350836Z

Source-reported events for the cited work

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Observation 6881a72f-9cec-429f-818d-6ca1fb08b7ee · outbound

This paper cites Bartoldson, James Diffenderfer, Konstantinos Parasyris, and Bhavya Kailkhura.

Intriguing Properties of Robust Classification Bartoldson, James Diffenderfer, Konstantinos Parasyris, and Bhavya Kailkhura

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.336101Z

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

Observation 7b04cc9a-3989-4f2b-a2af-3faada67ef45 · outbound

This paper cites Pay attention to your loss: understanding misconceptions about Lipschitz neural networks.

Intriguing Properties of Robust Classification Pay attention to your loss: understanding misconceptions about Lipschitz neural networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.322567Z

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Observation 93b4e849-96cc-46b7-9685-e975409be0dc · outbound

This paper cites Lower bounds on adversarial robustness from optimal trans- port.

Intriguing Properties of Robust Classification Lower bounds on adversarial robustness from optimal trans- port

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.306552Z

Source-reported events for the cited work

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Observation fc4b7a37-8f87-4c86-a6cd-41504082f469 · outbound

This paper cites Sample complexity of robust linear classification on sepa- rated data.

Intriguing Properties of Robust Classification Sample complexity of robust linear classification on sepa- rated data

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.285330Z

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 32182e04-fc91-4620-bb1a-75b318ba6158 · outbound

This paper cites Adversarial examples from computational con- straints.

Intriguing Properties of Robust Classification Adversarial examples from computational con- straints

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.269685Z

Source-reported events for the cited work

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Observation 05cf5a58-5244-43ab-bddf-8b68763b3bd4 · outbound

This paper cites A law of robustness for two-layers neural networks.

Intriguing Properties of Robust Classification A law of robustness for two-layers neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.253631Z

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 047bf134-6dab-4071-8bd8-0d698b709b9a · outbound

This paper cites Parseval networks: Improv- ing robustness to adversarial examples.

Intriguing Properties of Robust Classification Parseval networks: Improv- ing robustness to adversarial examples

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.239913Z

Source-reported events for the cited work

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Observation bf677fb7-04f1-4c60-9f57-dffb4ea6bf7b · outbound

This paper cites Certified adversarial robustness via randomized smoothing.

Intriguing Properties of Robust Classification Certified adversarial robustness via randomized smoothing

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.225681Z

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 eab2f57b-0771-4238-acc7-8f93143b18f7 · outbound

This paper cites Sharp sta- tistical guaratees for adversarially robust gaussian classifi- cation.

Intriguing Properties of Robust Classification Sharp sta- tistical guaratees for adversarially robust gaussian classifi- cation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.209672Z

Source-reported events for the cited work

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Observation a14181c1-0c75-4927-aab6-7db513f20572 · outbound

This paper cites Computational limitations in robust classification and win-win results.

Intriguing Properties of Robust Classification Computational limitations in robust classification and win-win results

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.194606Z

Source-reported events for the cited work

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Observation 7a3716a1-2f52-4673-aa5a-05017a9865d1 · outbound

This paper cites Generalized No Free Lunch Theorem for Adversarial Robustness.

Intriguing Properties of Robust Classification Generalized No Free Lunch Theorem for Adversarial Robustness

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-11T21:46:08.672334Z

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 26e9bfb1-d2cd-4744-ae39-8efc7215bbf6 · outbound

This paper cites Analy- sis of classifiers’ robustness to adversarial perturbations.Ma- chine Learning, 2018.

Intriguing Properties of Robust Classification Analy- sis of classifiers’ robustness to adversarial perturbations.Ma- chine Learning, 2018

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.180476Z

Source-reported events for the cited work

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Observation 192e4852-39e2-40ac-9216-3d7245485cac · outbound

This paper cites Explaining and harnessing adversarial examples.

Intriguing Properties of Robust Classification Explaining and harnessing adversarial examples

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.164686Z

Source-reported events for the cited work

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Observation 6ae7861b-25b1-417b-855a-f31a40c8f161 · outbound

This paper cites Improving robustness using generated data.

Intriguing Properties of Robust Classification Improving robustness using generated data

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.145614Z

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 b629f5e8-76c4-44a4-9395-62e78833ff33 · outbound

This paper cites Deep residual learning for image recognition.

Intriguing Properties of Robust Classification Deep residual learning for image recognition

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.116369Z

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 7f1dbb22-d55a-458c-b4ee-701644c05056 · outbound

This paper cites Unlocking deterministic robustness certification on Imagenet.

Intriguing Properties of Robust Classification Unlocking deterministic robustness certification on Imagenet

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.096764Z

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 334a364d-0373-4f32-9526-d9e9da52843f · outbound

This paper cites A recipe for improved certifiable robustness.

Intriguing Properties of Robust Classification A recipe for improved certifiable robustness

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.080526Z

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 661bf8e3-0734-413b-9693-3eb455a52b07 · outbound

This paper cites Adversar- ial examples are not bugs, they are features.

Intriguing Properties of Robust Classification Adversar- ial examples are not bugs, they are features

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.064721Z

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 3f5f3a37-61a5-4342-aa40-13575e91e99d · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal co- variate shift.

Intriguing Properties of Robust Classification Batch normalization: Accelerating deep network training by reducing internal co- variate shift

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.049891Z

Source-reported events for the cited work

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Observation b89d5961-5e0c-4515-87b0-c47a36ed9e6b · outbound

This paper cites an unresolved cited work.

Intriguing Properties of Robust Classification Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:46:09.034983Z

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 1faa86c1-4418-46e1-b8d0-2ac5fd54d6f6 · outbound

This paper cites Why robust generalization in deep learning is diffi- cult: Perspective of expressive power.

Intriguing Properties of Robust Classification Why robust generalization in deep learning is diffi- cult: Perspective of expressive power

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.021721Z

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 bd2d6a7a-17f0-4a6d-82eb-f7b12e0a2ead · outbound

This paper cites A dynamical system perspective for Lipschitz neural networks.

Intriguing Properties of Robust Classification A dynamical system perspective for Lipschitz neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:09.007593Z

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 57889c89-111d-4845-af66-d12896a8a140 · outbound

This paper cites The curious case of adversarially robust models: More data can help, double descend, or hurt generalization.

Intriguing Properties of Robust Classification The curious case of adversarially robust models: More data can help, double descend, or hurt generalization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.992258Z

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 839af719-fdca-402a-8857-99f6780c89ab · outbound

This paper cites Adversarial Robustness May Be at Odds With Simplicity.

Intriguing Properties of Robust Classification Adversarial Robustness May Be at Odds With Simplicity

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T21:46:08.505269Z

Source-reported events for the cited work

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Observation 3ef02021-91d4-47e3-9654-01db15f0b76b · outbound

This paper cites an unresolved cited work.

Intriguing Properties of Robust Classification Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:46:08.979009Z

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Observation e89aa632-cf95-4f25-8d45-d12bb8c87dd3 · outbound

This paper cites SimpleConvNet.

Intriguing Properties of Robust Classification SimpleConvNet

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.966739Z

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 02ec7cac-0564-4915-b051-669cb6668526 · outbound

This paper cites an unresolved cited work.

Intriguing Properties of Robust Classification Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:46:08.954979Z

Source-reported events for the cited work

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Observation b483c782-c923-4a39-9a60-d9d79309a1f5 · outbound

This paper cites 1-Lipschitz Neural Networks are more expressive with N-Activations.

Intriguing Properties of Robust Classification 1-Lipschitz Neural Networks are more expressive with N-Activations

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-11T21:46:08.636820Z

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 a37de3a9-dc10-4fb0-9c3b-d961160ab1f4 · outbound

This paper cites an unresolved cited work.

Intriguing Properties of Robust Classification Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:46:08.942778Z

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Observation 69630e69-0506-42d2-96f2-8a3441befa6d · outbound

This paper cites Adversarial training can hurt gen- eralization.

Intriguing Properties of Robust Classification Adversarial training can hurt gen- eralization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.930184Z

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 ec1c54bf-3be0-4999-b763-27b7a78cd169 · outbound

This paper cites Understanding and mitigating the tradeoff between robustness and accuracy.

Intriguing Properties of Robust Classification Understanding and mitigating the tradeoff between robustness and accuracy

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.917362Z

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

Observation 06146b40-6249-4efe-9864-24e240e03dd1 · outbound

This paper cites Adversarially robust gener- alization requires more data.

Intriguing Properties of Robust Classification Adversarially robust gener- alization requires more data

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.904254Z

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Observation 71295f6b-9109-4645-acb8-19156d77838d · outbound

This paper cites Understanding Machine Learning: From Theory to Algorithms.

Intriguing Properties of Robust Classification Understanding Machine Learning: From Theory to Algorithms

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.890865Z

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

Observation 3819c203-1694-4153-b8bc-1ab1b60610d4 · outbound

This paper cites In- triguing properties of neural networks.

Intriguing Properties of Robust Classification In- triguing properties of neural networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.876175Z

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 0cee9537-971c-4b14-b5cb-993c06213f81 · outbound

This paper cites Robustness may be at odds with accuracy.

Intriguing Properties of Robust Classification Robustness may be at odds with accuracy

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.862137Z

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 8237b94b-0849-4276-b14b-491d1a7dad46 · outbound

This paper cites Lipschitz-margin training: Scalable certification of pertur- bation invariance for deep neural networks.

Intriguing Properties of Robust Classification Lipschitz-margin training: Scalable certification of pertur- bation invariance for deep neural networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.848600Z

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 43a786d9-48ff-4ab3-aa5f-e637444b97b5 · outbound

This paper cites Lipschitz regularity of deep neural networks: analysis and efficient estimation.

Intriguing Properties of Robust Classification Lipschitz regularity of deep neural networks: analysis and efficient estimation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.835266Z

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 ac614304-bf93-4a2e-aff0-499b4ee51e78 · outbound

This paper cites Better diffusion models further improve adversarial training.

Intriguing Properties of Robust Classification Better diffusion models further improve adversarial training

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.822432Z

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 507dfa4a-fcd9-4868-b389-a6d2606d3a2c · outbound

This paper cites 94% on CIFAR-10 in 94 lines and 94 seconds.

Intriguing Properties of Robust Classification 94% on CIFAR-10 in 94 lines and 94 seconds

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.805644Z

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 aad141e6-a26e-4643-9d48-856e73e21b8a · outbound

This paper cites LOT: Layer-wise orthogo- nal training on improvingℓ2 certified robustness.

Intriguing Properties of Robust Classification LOT: Layer-wise orthogo- nal training on improvingℓ2 certified robustness

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.790324Z

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 41aff2b8-ec27-479c-b840-a8c23ab421ca · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy.

Intriguing Properties of Robust Classification Theoretically principled trade-off between robustness and accuracy

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.773086Z

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 4d3e5c9c-0a17-487c-92b1-9a370a79bcd9 · outbound

This paper cites Recall Theorem 2.

Intriguing Properties of Robust Classification Recall Theorem 2

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.758436Z

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 7b97cf02-f5ef-4f0f-b63a-525bb4595f6e · outbound

This paper cites an unresolved cited work.

Intriguing Properties of Robust Classification Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:46:08.745391Z

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 02f636a1-15a7-4ae2-80ed-2c72c3485d80 · outbound

This paper cites For the performance on additional subsets of prin- cipal components see Table 1 and Figure 7.

Intriguing Properties of Robust Classification For the performance on additional subsets of prin- cipal components see Table 1 and Figure 7

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.733699Z

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 c14b5293-2dde-4f5d-868e-de355261e484 · outbound

This paper cites Often the architecture, layers, and the training pipeline in general is different depending on whether accuracy or robust accuracy is the goal metric.

Intriguing Properties of Robust Classification Often the architecture, layers, and the training pipeline in general is different depending on whether accuracy or robust accuracy is the goal metric

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.716023Z

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 fd42b669-8b30-479d-945c-5da621f92cdf · outbound

This paper cites In this section we want to explore why this might be the case.

Intriguing Properties of Robust Classification In this section we want to explore why this might be the case

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:46:08.702755Z

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.

Pith citing papers

Observation 24a8236a-c857-43ed-ac22-59468d85589b · inbound

Concept-based Visual Counterfactual Explanations with Diffusion Models cites this paper.

Concept-based Visual Counterfactual Explanations with Diffusion Models Intriguing Properties of Robust Classification

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T14:57:31.353642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.