References in zbMATH (referenced in 12 articles )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Cao, Yi; Liu, Xiaoquan; Zhai, Jia: Option valuation under no-arbitrage constraints with neural networks (2021)
  2. Vasilis Nikolaidis: The nnlib2 library and nnlib2Rcpp R package for implementing neural networks (2021) not zbMATH
  3. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  4. Arya, Vijay; Bellamy, Rachel K. E.; Chen, Pin-Yu; Dhurandhar, Amit; Hind, Michael; Hoffman, Samuel C.; Houde, Stephanie; Liao, Q. Vera; Luss, Ronny; Mojsilović, Aleksandra; Mourad, Sami; Pedemonte, Pablo; Raghavendra, Ramya; Richards, John T.; Sattigeri, Prasanna; Shanmugam, Karthikeyan; Singh, Moninder; Varshney, Kush R.; Wei, Dennis; Zhang, Yunfeng: AI Explainability 360: an extensible toolkit for understanding data and machine learning models (2020)
  5. Begüm D. Topçuoğlu; Zena Lapp; Kelly L. Sovacool; Evan Snitkin; Jenna Wiens; Patrick D. Schloss: mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines (2020) not zbMATH
  6. D. van Kuppevelt, C. Meijer, F. Huber, A. van der Ploeg, S. Georgievska, V. T. van Hees: Mcfly: Automated deep learning on time series (2020) not zbMATH
  7. Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, Przemyslaw Biecek: dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python (2020) arXiv
  8. Rohan Anand, Joeran Beel: Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization (2020) arXiv
  9. Szymon Maksymiuk, Alicja Gosiewska, Przemyslaw Biecek: Landscape of R packages for eXplainable Artificial Intelligence (2020) arXiv
  10. Hubert Baniecki; Przemyslaw Biecek: modelStudio: Interactive Studio with Explanations for ML Predictive Models (2019) not zbMATH
  11. Mateusz Staniak, Przemyslaw Biecek: The Landscape of R Packages for Automated Exploratory Data Analysis (2019) arXiv
  12. Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin: CatBoost: gradient boosting with categorical features support (2018) arXiv