---
title: "MrYxJ/calculate-flops.pytorch: papers citing it"
description: "4 papers in the tracked journals link to the GitHub repository MrYxJ/calculate-flops.pytorch in their Crossref records, the first in 2025; most of them (2) in 2026."
url: https://scholar4ai.com/code/github-mryxj-calculate-flops-pytorch
markdown_url: https://scholar4ai.com/code/github-mryxj-calculate-flops-pytorch.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 MrYxJ/calculate-flops.pytorch in their Crossref records, the first in 2025; most of them (2) in 2026. (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)
---

# MrYxJ/calculate-flops.pytorch: papers linking to it

Research papers in the 480 tracked journals that link to MrYxJ/calculate-flops.pytorch (https://github.com/MrYxJ/calculate-flops.pytorch), 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
- 947 stars
- 44 forks
- Python
- license MIT
- created 2023
- https://github.com/MrYxJ/calculate-flops.pytorch

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

## Key findings

- 4 papers in the tracked journals link to the GitHub repository MrYxJ/calculate-flops.pytorch in their Crossref records, the first in 2025; most of them (2) in 2026.
- By category: AI (4); the journals with the most are Applied Intelligence (1), Neural Computing and Applications (1) and Neural Networks (1).

## By year

*Papers linking to MrYxJ/calculate-flops.pytorch by publication year*

| Year | Papers |
|---|---:|
| 2025 | 2 |
| 2026 | 2 |

## Journals

*Journals whose papers link to MrYxJ/calculate-flops.pytorch most*

| Journal | Papers |
|---|---:|
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 1 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 |

## Papers

*Papers linking to MrYxJ/calculate-flops.pytorch, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2025-07-15 | [STREAM-Net: Spatio-temporal feature fusion network for robust rPPG signal measurement in remote health monitoring](https://doi.org/10.1016/j.knosys.2025.114080) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Muhammad Usman, Milena Sobotka, Jacek Rumiński | 4 | cites it |
| 2025-08-23 | [MSFI: Multi-timescale spatio-temporal features integration in spiking neural networks](https://doi.org/10.1016/j.neunet.2025.108024) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Dengfeng Xue, Wenjuan Li, Chunfeng Yuan et al. | 1 | cites it |
| 2026-02-01 | [Temporal modeling with reversible transformers](https://doi.org/10.1007/s00521-025-11707-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Л. Р. Кулик | 1 | cites it |
| 2026-05-01 | [Audio–visual segmentation via hierarchical side tuning with state space model](https://doi.org/10.1007/s10489-026-07249-3) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Wenyi Xia, Qingwei Geng, Xiaodong Gu | 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). MrYxJ/calculate-flops.pytorch: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-mryxj-calculate-flops-pytorch

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