Scholar4AI Updated 2026-11-03 14:43 UTC+09:00

rstudio/keras3: papers linking to it

GitHub · 857 stars · 279 forks · R · created 2017 · github.com/rstudio/keras3

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to rstudio/keras3 (https://github.com/rstudio/keras3), 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.

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Key findings#

  • 9 papers in the tracked journals link to the GitHub repository rstudio/keras3 in their Crossref records, the first in 2021; most of them (3) in 2024.
  • By category: Finance (6), Statistics (2) and AI (1); the journals with the most are European Actuarial Journal (6), Journal of Statistical Theory and Practice (1) and Japanese Journal of Statistics and Data Science (1).
  • 6 of the papers build on one another: a later paper cites an earlier one that also links rstudio/keras3.

By year#

2021: 2 papers2022: 2 papers2023: 1 paper2024: 3 papers2025: 1 paper
20212025
Papers linking to rstudio/keras3 by publication year
YearPapers
20212
20222
20231
20243
20251

Journals#

Journals whose papers link to rstudio/keras3 most
JournalPapers
European Actuarial Journal6
Journal of Statistical Theory and Practice1
Japanese Journal of Statistics and Data Science1
Neural Computing and Applications1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Papers linking to rstudio/keras3, most cited first
PublishedTitleJournalAuthorsCitationsLink
2022-04-02Efficient use of data for LSTM mortality forecastingEuropean Actuarial JournalMathias Lindholm, Lina Palmborg31cites it
2023-11-08A multi-task network approach for calculating discrimination-free insurance pricesEuropean Actuarial JournalMathias Lindholm, Ronald Richman, Andreas Tsanakas et al.14cites it
2021-01-29Deep neural networks architecture driven by problem-specific informationNeural Computing and ApplicationsDaniel Urda, Francisco J. Veredas, Javier González-Enrique et al.12cites it
2022-05-12Micro-level prediction of outstanding claim counts based on novel mixture models and neural networksEuropean Actuarial JournalAxel Bücher, Alexander Rosenstock12cites it
2024-03-06A neural network approach for the mortality analysis of multiple populations: a case study on data of the Italian populationEuropean Actuarial JournalMaximilian Euthum, Matthias A. Scherer, Francesco Ungolo9cites it
2025-02-15The credibility transformerEuropean Actuarial JournalRonald Richman, Salvatore Scognamiglio, Mario V. Wüthrich7cites it
2024-02-25High-cardinality categorical covariates in network regressionsJapanese Journal of Statistics and Data ScienceRonald Richman, Mario V. Wüthrich5cites it
2021-05-25On Ensembles, I-Optimality, and Active LearningJournal of Statistical Theory and PracticeWilliam D. Heavlin2cites it
2024-04-24Combined modelling of micro-level outstanding claim counts and individual claim frequencies in non-life insuranceEuropean Actuarial JournalAxel Bücher, Alexander Rosenstock0cites 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). rstudio/keras3: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-rstudio-keras3

@misc{scholar4ai-code-github-rstudio-keras3,
  author = {{Scholar4AI}},
  title = {rstudio/keras3: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-rstudio-keras3}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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