---
title: "google/qkeras: papers citing this GitHub repository"
description: "4 papers in the tracked journals link to the GitHub repository google/qkeras in their Crossref records, the first in 2021; most of them (2) in 2021."
url: https://scholar4ai.com/code/github-google-qkeras
markdown_url: https://scholar4ai.com/code/github-google-qkeras.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 google/qkeras in their Crossref records, the first in 2021; most of them (2) in 2021. (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)
---

# google/qkeras: papers linking to it

Research papers in the 480 tracked journals that link to google/qkeras (https://github.com/google/qkeras), 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
- 585 stars
- 108 forks
- Python
- license Apache-2.0
- created 2019
- https://github.com/google/qkeras

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

## Key findings

- 4 papers in the tracked journals link to the GitHub repository google/qkeras in their Crossref records, the first in 2021; most of them (2) in 2021.
- By category: AI (4); the journals with the most are Nature Machine Intelligence (3) and Neural Computing and Applications (1).
- 4 of the papers build on one another: a later paper cites an earlier one that also links google/qkeras.

## By year

*Papers linking to google/qkeras by publication year*

| Year | Papers |
|---|---:|
| 2021 | 2 |
| 2022 | 1 |
| 2025 | 1 |

## Journals

*Journals whose papers link to google/qkeras most*

| Journal | Papers |
|---|---:|
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 3 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://doi.org/10.1038/s42256-021-00356-5) (Nature Machine Intelligence, 2021)
  - [Resource-efficient inference for particle physics](https://doi.org/10.1038/s42256-021-00381-4) (Nature Machine Intelligence, 2021) — cites the paper above
  - [Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider](https://doi.org/10.1038/s42256-022-00441-3) (Nature Machine Intelligence, 2022) — cites the paper above
  - [Hardware implementation of a quantized CNN model for early detection of skin cancer cells using hls4ml tool](https://doi.org/10.1007/s00521-025-11411-2) (Neural Computing and Applications, 2025) — cites the paper above

## Papers

*Papers linking to google/qkeras, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-06-21 | [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://doi.org/10.1038/s42256-021-00356-5) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Claudionor N. Coelho, Aki Kuusela, Shan Li et al. | 226 | cites it |
| 2022-02-23 | [Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider](https://doi.org/10.1038/s42256-022-00441-3) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Ekaterina Govorkova, Ema Puljak, T. K. Aarrestad et al. | 79 | cites it |
| 2025-07-21 | [Hardware implementation of a quantized CNN model for early detection of skin cancer cells using hls4ml tool](https://doi.org/10.1007/s00521-025-11411-2) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Ehsan Arefi, Morteza Mousazadeh | 1 | cites it |
| 2021-08-17 | [Resource-efficient inference for particle physics](https://doi.org/10.1038/s42256-021-00381-4) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | David Rousseau | 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). google/qkeras: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-google-qkeras

```bibtex
@misc{scholar4ai-code-github-google-qkeras,
  author = {{Scholar4AI}},
  title = {google/qkeras: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-google-qkeras}},
  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/).