---
title: "MadryLab/robustness: papers citing this GitHub repository"
description: "9 papers in the tracked journals link to the GitHub repository MadryLab/robustness in their Crossref records, the first in 2021; most of them (3) in 2024."
url: https://scholar4ai.com/code/github-madrylab-robustness
markdown_url: https://scholar4ai.com/code/github-madrylab-robustness.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "9 papers in the tracked journals link to the GitHub repository MadryLab/robustness in their Crossref records, the first in 2021; most of them (3) in 2024. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# MadryLab/robustness: papers linking to it

Research papers in the 480 tracked journals that link to MadryLab/robustness (https://github.com/MadryLab/robustness), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 947 stars
- 179 forks
- Jupyter Notebook
- license MIT
- created 2019
- https://github.com/MadryLab/robustness

All repositories: https://scholar4ai.com/code.md

## Key findings

- 9 papers in the tracked journals link to the GitHub repository MadryLab/robustness in their Crossref records, the first in 2021; most of them (3) in 2024.
- By category: AI (8) and Operations Research (1); the journals with the most are International Journal of Computer Vision (3), Pattern Recognition (2) and Machine Learning (2).
- 4 of the papers build on one another: a later paper cites an earlier one that also links MadryLab/robustness.

## By year

*Papers linking to MadryLab/robustness by publication year*

| Year | Papers |
|---|---:|
| 2021 | 1 |
| 2022 | 2 |
| 2023 | 1 |
| 2024 | 3 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to MadryLab/robustness most*

| Journal | Papers |
|---|---:|
| [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 3 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 2 |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 2 |
| [Optimization and Engineering](https://scholar4ai.com/journals/optimization-and-engineering.md) | 1 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Aliasing and adversarial robust generalization of CNNs](https://doi.org/10.1007/s10994-022-06222-8) (Machine Learning, 2022)
  - [Fix Your Downsampling ASAP! Aliasing and Sinc Artifact Free Pooling in the Fourier Domain](https://doi.org/10.1007/s11263-026-03005-9) (International Journal of Computer Vision, 2026) — cites the paper above
- [A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking](https://doi.org/10.1007/s11263-024-02196-3) (International Journal of Computer Vision, 2024)
  - [On the robustness of adversarial training against uncertainty attacks](https://doi.org/10.1016/j.patcog.2025.112519) (Pattern Recognition, 2025) — cites the paper above

## Papers

*Papers linking to MadryLab/robustness, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-08-09 | [A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking](https://doi.org/10.1007/s11263-024-02196-3) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Chang Liu, Yinpeng Dong, Wenzhao Xiang et al. | 104 | cites it |
| 2023-03-21 | [Exploring misclassifications of robust neural networks to enhance adversarial attacks](https://doi.org/10.1007/s10489-023-04532-5) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Leo Schwinn, René Raab, An Nguyen et al. | 66 | cites it |
| 2022-09-23 | [ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches](https://doi.org/10.1016/j.patcog.2022.109064) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Maura Pintor, Daniele Angioni, Angelo Sotgiu et al. | 48 | cites it |
| 2022-08-26 | [Aliasing and adversarial robust generalization of CNNs](https://doi.org/10.1007/s10994-022-06222-8) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Julia Grabinski, Janis Keuper, Margret Keuper | 16 | cites it |
| 2021-06-30 | [An empirical study of derivative-free-optimization algorithms for targeted black-box attacks in deep neural networks](https://doi.org/10.1007/s11081-021-09652-w) | [Optimization and Engineering](https://scholar4ai.com/journals/optimization-and-engineering.md) | Giuseppe Ughi, Vinayak Abrol, Jared Tanner | 15 | cites it |
| 2024-06-19 | [Improving interpretability via regularization of neural activation sensitivity](https://doi.org/10.1007/s10994-024-06549-4) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Ofir Moshe, Gil Fidel, Ron Bitton et al. | 9 | cites it |
| 2024-06-24 | [RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation](https://doi.org/10.1007/s11263-024-02133-4) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Utkarsh Nath, Yan-Cheng Wang, Pavan K. Turaga et al. | 6 | cites it |
| 2025-10-03 | [On the robustness of adversarial training against uncertainty attacks](https://doi.org/10.1016/j.patcog.2025.112519) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Emanuele Ledda, Giovanni Scodeller, Daniele Angioni et al. | 3 | cites it |
| 2026-10-01 | [Fix Your Downsampling ASAP! Aliasing and Sinc Artifact Free Pooling in the Fourier Domain](https://doi.org/10.1007/s11263-026-03005-9) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Julia Grabinski, Steffen Jung, Janis Keuper et al. | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). MadryLab/robustness: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-madrylab-robustness

```bibtex
@misc{scholar4ai-code-github-madrylab-robustness,
  author = {{Scholar4AI}},
  title = {MadryLab/robustness: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-madrylab-robustness}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).