kuangliu/pytorch-cifar: papers linking to it

GitHub · 6,448 stars · 2,162 forks · Python · license MIT · created 2017 · github.com/kuangliu/pytorch-cifar

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to kuangliu/pytorch-cifar (https://github.com/kuangliu/pytorch-cifar), 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.

Key findings#

4 papers in the tracked journals link to the GitHub repository kuangliu/pytorch-cifar in their Crossref records, the first in 2022; most of them (3) in 2022.
By category: AI (4); the journals with the most are Applied Intelligence (1), Pattern Recognition (1) and Machine Learning (1).

By year#

20222025

Papers linking to kuangliu/pytorch-cifar by publication year
Year	Papers
2022	3
2025	1

Journals#

Journals whose papers link to kuangliu/pytorch-cifar most
Journal	Papers
Applied Intelligence	1
Pattern Recognition	1
Machine Learning	1
International Journal of Computer Vision	1

Papers linking to kuangliu/pytorch-cifar, most cited first
Published	Title	Journal	Authors	Citations	Link
2022-10-02	Distribution-Sensitive Information Retention for Accurate Binary Neural Network	International Journal of Computer Vision	Haotong Qin, Xiangguo Zhang, Ruihao Gong et al.	143	cites it
2022-10-14	Black-box Bayesian adversarial attack with transferable priors	Machine Learning	Shudong Zhang, Haichang Gao, Chao Shu et al.	6	cites it
2022-10-15	Gradient-only surrogate to resolve learning rates for robust and consistent training of deep neural networks	Applied Intelligence	Younghwan Chae, Daniel Nicolas Wilke, Dominic Kafka	5	cites it
2025-12-05	Regularized evidential neural networks for deep active learning	Pattern Recognition	Pengju Wang, Sicong Zhang, Rongrong Chen et al.	4	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). kuangliu/pytorch-cifar: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-kuangliu-pytorch-cifar

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

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