raghakot/keras-vis: papers linking to it

GitHub · 2,990 stars · 632 forks · Python · license MIT · created 2016 · github.com/raghakot/keras-vis

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to raghakot/keras-vis (https://github.com/raghakot/keras-vis), 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 raghakot/keras-vis in their Crossref records, the first in 2019; most of them (2) in 2020.
By category: AI (4); the journals with the most are Neural Computing and Applications (2), Applied Intelligence (1) and Nature Machine Intelligence (1).

By year#

20192021

Papers linking to raghakot/keras-vis by publication year
Year	Papers
2019	1
2020	2
2021	1

Journals#

Journals whose papers link to raghakot/keras-vis most
Journal	Papers
Neural Computing and Applications	2
Applied Intelligence	1
Nature Machine Intelligence	1

Papers linking to raghakot/keras-vis, most cited first
Published	Title	Journal	Authors	Citations	Link
2020-02-14	Deep-learning-based prediction of late age-related macular degeneration progression	Nature Machine Intelligence	Qi Yan, Daniel E. Weeks, Hongyi Xin et al.	147	cites it
2019-08-09	Deep-PHURIE: deep learning based hurricane intensity estimation from infrared satellite imagery	Neural Computing and Applications	Muhammad Dawood, Amina Asif, Fayyaz Minhas	51	cites it
2021-01-10	EmNet: a deep integrated convolutional neural network for facial emotion recognition in the wild	Applied Intelligence	Sumeet Saurav, Ravi Saini, Sanjay Kumar Singh	46	cites it
2020-10-01	HihO: accelerating artificial intelligence interpretability for medical imaging in IoT applications using hierarchical occlusion	Neural Computing and Applications	William S. Monroe, Frank Michael Skidmore, David G. Odaibo et al.	16	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). raghakot/keras-vis: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-raghakot-keras-vis

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

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