---
title: "Lightning-AI/pytorch-lightning: papers citing it"
description: "13 papers in the tracked journals link to the GitHub repository Lightning-AI/pytorch-lightning in their Crossref records, the first in 2022; most of them (6) in 2025."
url: https://scholar4ai.com/code/github-lightning-ai-pytorch-lightning
markdown_url: https://scholar4ai.com/code/github-lightning-ai-pytorch-lightning.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "13 papers in the tracked journals link to the GitHub repository Lightning-AI/pytorch-lightning in their Crossref records, the first in 2022; most of them (6) in 2025. (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)
---

# Lightning-AI/pytorch-lightning: papers linking to it

Research papers in the 480 tracked journals that link to Lightning-AI/pytorch-lightning (https://github.com/Lightning-AI/pytorch-lightning), 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
- 31,439 stars
- 3,843 forks
- Python
- license Apache-2.0
- created 2019
- https://github.com/Lightning-AI/pytorch-lightning

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

## Key findings

- 13 papers in the tracked journals link to the GitHub repository Lightning-AI/pytorch-lightning in their Crossref records, the first in 2022; most of them (6) in 2025.
- By category: AI (13); the journals with the most are Nature Machine Intelligence (4), Applied Intelligence (3) and Neural Networks (2).
- Linked repositories derived from it: 1, such as PyTorchLightning/pytorch-lightning: 0.7.6 release.

## By year

*Papers linking to Lightning-AI/pytorch-lightning by publication year*

| Year | Papers |
|---|---:|
| 2022 | 1 |
| 2023 | 4 |
| 2024 | 1 |
| 2025 | 6 |
| 2026 | 1 |

## Journals

*Journals whose papers link to Lightning-AI/pytorch-lightning most*

| Journal | Papers |
|---|---:|
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 4 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 3 |
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 2 |
| [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 1 |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 1 |

## Related repositories

*Linked repositories Lightning-AI/pytorch-lightning derives from or that derive from it*

| Repository | Relation | Papers |
|---|---|---:|
| [PyTorchLightning/pytorch-lightning: 0.7.6 release](https://scholar4ai.com/code/zenodo-3828935.md) | Zenodo archive of Lightning-AI/pytorch-lightning | 7 |

## Papers

*Papers linking to Lightning-AI/pytorch-lightning, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-10-23 | [Neural scaling of deep chemical models](https://doi.org/10.1038/s42256-023-00740-3) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Nathan C. Frey, Ryan Soklaski, Simon Axelrod et al. | 125 | cites it |
| 2023-11-06 | [Development of the Senseiver for efficient field reconstruction from sparse observations](https://doi.org/10.1038/s42256-023-00746-x) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Javier E. Santos, Zachary Fox, Arvind T. Mohan et al. | 101 | cites it |
| 2022-10-03 | [Physically constrained generative adversarial networks for improving precipitation fields from Earth system models](https://doi.org/10.1038/s42256-022-00540-1) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Philipp Hess, Markus Drüke, Stefan Petri et al. | 93 | cites it |
| 2025-05-23 | [Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs](https://doi.org/10.1038/s42256-025-01031-9) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Daniil A. Boiko, Thiago Reschützegger, Benjamín Sánchez-Lengeling et al. | 25 | cites it |
| 2024-08-20 | [Performance metrics for multi-step forecasting measuring win-loss, seasonal variance and forecast stability: an empirical study](https://doi.org/10.1007/s10489-024-05715-4) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Eivind Strøm, Odd Erik Gundersen | 20 | cites it |
| 2023-06-21 | [Auto CNN classifier based on knowledge transferred from self-supervised model](https://doi.org/10.1007/s10489-023-04598-1) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Jaydeep Kishore, Snehasis Mukherjee | 8 | cites it |
| 2023-12-29 | [SyDog-Video: A Synthetic Dog Video Dataset for Temporal Pose Estimation](https://doi.org/10.1007/s11263-023-01946-z) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Moira Shooter, Charles Malleson, Adrian Hilton | 7 | cites it |
| 2025-11-25 | [Interpretable Deep Reinforcement Learning Via Concept-Based Policy Distillation](https://doi.org/10.1007/s10994-025-06928-5) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Yanzhe Bekkemoen, Helge Langseth A | 5 | cites it |
| 2025-04-16 | [Deep learning solutions for audio event detection in a swine barn using environmental audio and weak labels](https://doi.org/10.1007/s10489-025-06555-6) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | André Moreira Souza, Lori Kobayashi, Lucas Tassoni Andrietta et al. | 3 | cites it |
| 2025-07-21 | [CoSTI: Consistency models for (a faster) spatio-temporal imputation](https://doi.org/10.1016/j.knosys.2025.114117) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Javier Solí­s-Garcí­a, Belén Vega-Márquez, Juan A. Nepomuceno et al. | 2 | cites it |
| 2025-08-23 | [Raw event-based adversarial attacks for Spiking Neural Networks with configurable latencies](https://doi.org/10.1016/j.neunet.2025.108026) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Xiao Du, Wanli Shi, Xiaohan Zhao et al. | 0 | cites it |
| 2025-08-28 | [Focal Sampling: SGD biased towards early important samples for efficient image classification with augmentation selection](https://doi.org/10.1007/s10115-025-02563-7) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Alessio Quercia, Fernanda Nader, Abigail Morrison et al. | 0 | cites it |
| 2026-06-03 | [Symplectic convolutional neural networks](https://doi.org/10.1016/j.neunet.2026.109208) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Süleyman Yıldız, Konrad Janik, Peter Benner | 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). Lightning-AI/pytorch-lightning: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-lightning-ai-pytorch-lightning

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