Paper Citation Record · LEDGER

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function

As of 15 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2507.22446.

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

pith.paper-citation-record.v1
2507.22446 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:48:52.826310Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

34 of 34 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca2f0243-4cf6-4ddd-897b-42c109b07663 · outbound

This paper cites Deep residual learning for image recognition.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Deep residual learning for image recognition

Reference 1

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Observation c3db2741-e57f-4a68-8224-07202fd7d6e5 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Imagenet classification with deep convolutional neural networks

Reference 2

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Observation e53dcc63-b668-42e6-92cc-8cf7de44ebc3 · outbound

This paper cites Attention is all you need.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Attention is all you need

Reference 3

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Observation 7616c9e1-5133-4ec6-96fc-dd910bbe9175 · outbound

This paper cites Deep speech 2: End-to-end speech recognition in english and mandarin.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Deep speech 2: End-to-end speech recognition in english and mandarin

Reference 4

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Observation 13d37fd3-d2a9-46b0-94ad-8cc352ac9fb7 · outbound

This paper cites Intriguing properties of neural networks.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Intriguing properties of neural networks

Reference 5

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Observation 052efff8-24dd-4c55-8e48-5adf4ab6953a · outbound

This paper cites GPT-4 Technical Report.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function GPT-4 Technical Report

Reference 6

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Observation 65d2ceaa-61c2-44fa-bdb4-02368092431d · outbound

This paper cites Explaining and harnessing adversarial examples.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Explaining and harnessing adversarial examples

Reference 7

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Observation 7809ef6c-a8f7-4745-9bde-9b7255e8f494 · outbound

This paper cites Deepfool: a simple and accurate method to fool deep neural networks.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Deepfool: a simple and accurate method to fool deep neural networks

Reference 8

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Observation 0f337d16-b872-449f-8589-e99369fbda6a · outbound

This paper cites Carlini and D.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Carlini and D

Reference 9

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This paper cites To- wards deep learning models resistant to adversarial attacks.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function To- wards deep learning models resistant to adversarial attacks

Reference 10

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RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Unresolved cited work

Reference 11

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Observation 68452311-431f-4693-861c-524105ac6caa · outbound

This paper cites Generating adversarial examples with adversarial networks.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Generating adversarial examples with adversarial networks

Reference 12

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Source-reported events for the cited work

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Observation a9f504a1-91c8-4d2b-8bcd-82b519062957 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 13

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Observation 6a859323-2d34-4dea-8380-e59bf01dca7b · outbound

This paper cites Adversarial training for free! In Advances in Neural Information Processing Systems, volume 32, pages 3358–3369, 2019.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Adversarial training for free! In Advances in Neural Information Processing Systems, volume 32, pages 3358–3369, 2019

Reference 14

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Observation 72684c90-8a23-4b93-ba75-83c6e82472d4 · outbound

This paper cites Are labels required for improving adversarial robustness? In Advances in Neural Information Processing Systems, volume 32, 2019.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Are labels required for improving adversarial robustness? In Advances in Neural Information Processing Systems, volume 32, 2019

Reference 15

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Observation 12506a07-b444-41a8-b9b9-32410e39aac0 · outbound

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

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Theoreti- cally principled trade-off between robustness and accuracy

Reference 16

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Observation 48b91b83-8d58-43c8-ad84-5c70e38c5b57 · outbound

This paper cites Boosting adversarial training with hypersphere embedding.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Boosting adversarial training with hypersphere embedding

Reference 17

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Observation 9d248bb9-459d-4058-abd7-c4947d3e0f1a · outbound

This paper cites Improving adver- sarial robustness requires revisiting misclassified examples.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Improving adver- sarial robustness requires revisiting misclassified examples

Reference 18

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Observation c3f45a5d-a34d-4853-af8d-2fba9ef2361a · outbound

This paper cites Adversarial weight perturbation helps robust generalization.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Adversarial weight perturbation helps robust generalization

Reference 19

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Observation 3c4df498-4432-479b-831e-1d7f7e4258e8 · outbound

This paper cites Do Wider Neural Networks Really Help Adversarial Robustness?.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Do Wider Neural Networks Really Help Adversarial Robustness?

Reference 20

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Observation a843a0ed-0859-4960-a7ef-1cd777250081 · outbound

This paper cites Lafeat: Piercing through adversarial defenses with latent features.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Lafeat: Piercing through adversarial defenses with latent features

Reference 21

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Observation 212f8006-cf21-4485-b819-cbc42b327ffb · outbound

This paper cites Mora: Improving ensemble robustness evaluation with model reweighing attack.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Mora: Improving ensemble robustness evaluation with model reweighing attack

Reference 22

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Observation 33e8c301-b914-4c9e-a15f-45f08dfca9d3 · outbound

This paper cites Lafit: Efficient and reliable evaluation of adversarial defenses with latent features.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Lafit: Efficient and reliable evaluation of adversarial defenses with latent features

Reference 23

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Observation 894caea6-b147-443e-b3ad-11f2ff33e130 · outbound

This paper cites Boosting adversarial training with hypersphere embedding.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Boosting adversarial training with hypersphere embedding

Reference 24

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Observation fa99d9dd-256a-43a7-9e8d-242a0cb94c1f · outbound

This paper cites Fixing Data Augmentation to Improve Adversarial Robustness.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Fixing Data Augmentation to Improve Adversarial Robustness

Reference 25

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Observation 3c8938c2-9568-4c63-a36c-3669f80cd03c · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 26

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

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Theo- retically principled trade-off between robustness and accuracy

Reference 27

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Observation 996d7398-457a-4a02-936e-515c4c7d6af1 · outbound

This paper cites Improving adver- sarial robustness requires revisiting misclassified examples.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Improving adver- sarial robustness requires revisiting misclassified examples

Reference 28

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Observation 15fd2450-ae4b-4528-9919-2862647a8517 · outbound

This paper cites Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Reference 29

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This paper cites Mitigating Adversarial Effects Through Randomization.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Mitigating Adversarial Effects Through Randomization

Reference 30

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This paper cites Gaussian Error Linear Units (GELUs).

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Gaussian Error Linear Units (GELUs)

Reference 31

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Observation 83f82b13-b7ea-42a0-920f-fdfc23507a48 · outbound

This paper cites Adversarial weight perturbation helps robust generalization.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Adversarial weight perturbation helps robust generalization

Reference 32

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Observation e2abe9ec-3acd-4072-bc0f-897bda546515 · outbound

This paper cites Searching for Activation Functions.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Searching for Activation Functions

Reference 33

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Observation d9452f31-8fc2-4c23-8184-0c1c8d48d984 · outbound

This paper cites Lecture notes for machine learning theory.

RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function Lecture notes for machine learning theory

Reference 34

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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.