Scholar4AI Updated 2026-11-03 14:43 UTC+09:00

MadryLab/robustness: papers linking to it

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

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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.

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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#

2021: 1 paper2022: 2 papers2023: 1 paper2024: 3 papers2025: 1 paper2026: 1 paper
20212026
Papers linking to MadryLab/robustness by publication year
YearPapers
20211
20222
20231
20243
20251
20261

Journals#

Journals whose papers link to MadryLab/robustness most
JournalPapers
International Journal of Computer Vision3
Pattern Recognition2
Machine Learning2
Optimization and Engineering1
Applied Intelligence1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Papers linking to MadryLab/robustness, most cited first
PublishedTitleJournalAuthorsCitationsLink
2024-08-09A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and RethinkingInternational Journal of Computer VisionChang Liu, Yinpeng Dong, Wenzhao Xiang et al.104cites it
2023-03-21Exploring misclassifications of robust neural networks to enhance adversarial attacksApplied IntelligenceLeo Schwinn, René Raab, An Nguyen et al.66cites it
2022-09-23ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patchesPattern RecognitionMaura Pintor, Daniele Angioni, Angelo Sotgiu et al.48cites it
2022-08-26Aliasing and adversarial robust generalization of CNNsMachine LearningJulia Grabinski, Janis Keuper, Margret Keuper16cites it
2021-06-30An empirical study of derivative-free-optimization algorithms for targeted black-box attacks in deep neural networksOptimization and EngineeringGiuseppe Ughi, Vinayak Abrol, Jared Tanner15cites it
2024-06-19Improving interpretability via regularization of neural activation sensitivityMachine LearningOfir Moshe, Gil Fidel, Ron Bitton et al.9cites it
2024-06-24RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge DistillationInternational Journal of Computer VisionUtkarsh Nath, Yan-Cheng Wang, Pavan K. Turaga et al.6cites it
2025-10-03On the robustness of adversarial training against uncertainty attacksPattern RecognitionEmanuele Ledda, Giovanni Scodeller, Daniele Angioni et al.3cites it
2026-10-01Fix Your Downsampling ASAP! Aliasing and Sinc Artifact Free Pooling in the Fourier DomainInternational Journal of Computer VisionJulia Grabinski, Steffen Jung, Janis Keuper et al.0cites 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

@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}
}

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