---
title: "raghakot/keras-vis: papers citing this GitHub repository"
description: "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."
url: https://scholar4ai.com/code/github-raghakot-keras-vis
markdown_url: https://scholar4ai.com/code/github-raghakot-keras-vis.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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. (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)
---

# raghakot/keras-vis: papers linking to it

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.

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

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

## 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

*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](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 1 |

## Papers

*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](https://doi.org/10.1038/s42256-020-0154-9) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 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](https://doi.org/10.1007/s00521-019-04410-7) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 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](https://doi.org/10.1007/s10489-020-02125-0) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 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](https://doi.org/10.1007/s00521-020-05379-4) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 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

```bibtex
@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}
}
```

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