---
title: "canyilu/Tensor-tensor-product-toolbox: papers citing it"
description: "4 papers in the tracked journals link to the GitHub repository canyilu/Tensor-tensor-product-toolbox in their Crossref records, the first in 2020; most of them (1) in 2026."
url: https://scholar4ai.com/code/github-canyilu-tensor-tensor-product-toolbox
markdown_url: https://scholar4ai.com/code/github-canyilu-tensor-tensor-product-toolbox.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 canyilu/Tensor-tensor-product-toolbox in their Crossref records, the first in 2020; most of them (1) 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)
---

# canyilu/Tensor-tensor-product-toolbox: papers linking to it

Research papers in the 480 tracked journals that link to canyilu/Tensor-tensor-product-toolbox (https://github.com/canyilu/Tensor-tensor-product-toolbox), 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
- 97 stars
- 45 forks
- MATLAB
- created 2018
- https://github.com/canyilu/Tensor-tensor-product-toolbox

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

## Key findings

- 4 papers in the tracked journals link to the GitHub repository canyilu/Tensor-tensor-product-toolbox in their Crossref records, the first in 2020; most of them (1) in 2026.
- By category: Operations Research (3) and Statistics (1); the journals with the most are Journal of Optimization Theory and Applications (1), Journal of Global Optimization (1) and Annals of the Institute of Statistical Mathematics (1).

## By year

*Papers linking to canyilu/Tensor-tensor-product-toolbox by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2023 | 1 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to canyilu/Tensor-tensor-product-toolbox most*

| Journal | Papers |
|---|---:|
| [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | 1 |
| [Journal of Global Optimization](https://scholar4ai.com/journals/journal-of-global-optimization.md) | 1 |
| [Annals of the Institute of Statistical Mathematics](https://scholar4ai.com/journals/annals-of-the-institute-of-statistical-mathematics.md) | 1 |
| [Computational Optimization and Applications](https://scholar4ai.com/journals/computational-optimization-and-applications.md) | 1 |

## Papers

*Papers linking to canyilu/Tensor-tensor-product-toolbox, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-01-20 | [Tensor neural network models for tensor singular value decompositions](https://doi.org/10.1007/s10589-020-00167-1) | [Computational Optimization and Applications](https://scholar4ai.com/journals/computational-optimization-and-applications.md) | Xuezhong Wang, Maolin Che, Yimin Wei | 63 | cites it |
| 2023-03-02 | [A Randomized Singular Value Decomposition for Third-Order Oriented Tensors](https://doi.org/10.1007/s10957-023-02177-5) | [Journal of Optimization Theory and Applications](https://scholar4ai.com/journals/journal-of-optimization-theory-and-applications.md) | Minghui Ding, Yimin Wei, Pengpeng Xie | 5 | cites it |
| 2025-10-21 | [Generalized high-dimensional tensor learning with nuclear norm regularization](https://doi.org/10.1007/s10463-025-00960-x) | [Annals of the Institute of Statistical Mathematics](https://scholar4ai.com/journals/annals-of-the-institute-of-statistical-mathematics.md) | Weilin Shen, Yao Wang, Xuehu Zhu | 1 | cites it |
| 2026-01-30 | [An optimization approach for transformed low tubal rank of third-order tensors](https://doi.org/10.1007/s10898-025-01560-y) | [Journal of Global Optimization](https://scholar4ai.com/journals/journal-of-global-optimization.md) | Jinjie Liu, Chen Ling, Erbo Zhao et al. | 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). canyilu/Tensor-tensor-product-toolbox: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-canyilu-tensor-tensor-product-toolbox

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