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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Probabilistic Predictions with Federated Learning
Adam Thor Thorgeirsson, Frank Gauterin
Entropy (2020) Vol. 23, Iss. 1, pp. 41-41
Open Access | Times Cited: 16

Showing 16 citing articles:

Fusion of Probability Density Functions
Günther Koliander, Yousef El-Laham, Petar M. Djurić, et al.
Proceedings of the IEEE (2022) Vol. 110, Iss. 4, pp. 404-453
Open Access | Times Cited: 74
A review of predictive uncertainty estimation with machine learning
Hristos Tyralis, Georgia Papacharalampous
Artificial Intelligence Review (2024) Vol. 57, Iss. 4
Open Access | Times Cited: 25
Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles With Federated Learning
Adam Thor Thorgeirsson, Stefan Scheubner, Sebastian Fünfgeld, et al.
IEEE Open Journal of Vehicular Technology (2021) Vol. 2, pp. 151-161
Open Access | Times Cited: 42
Fuzzy Consensus With Federated Learning Method in Medical Systems
Dawid Poap
IEEE Access (2021) Vol. 9, pp. 150383-150392
Open Access | Times Cited: 35
A Federated Learning Approach Towards a Privacy-Preserving Technique for Brain Tumor Classification
Anurag De, Gautam Pal, Karnam Shyam, et al.
Lecture notes in networks and systems (2025), pp. 259-271
Closed Access
Probabilistic prediction model for failure mode of RC columns based on a two-stage method combining machine learning and Bayesian classifier
Rou-Han Li, Xiangyang Zhu, Shoushui Wei, et al.
Structures (2025) Vol. 74, pp. 108462-108462
Closed Access
Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition
Danilo Franco, Luca Oneto, Nicolò Navarin, et al.
Entropy (2021) Vol. 23, Iss. 8, pp. 1047-1047
Open Access | Times Cited: 15
Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources
Małgorzata Przybyła–Kasperek, Kwabena Frimpong Marfo
Entropy (2021) Vol. 23, Iss. 12, pp. 1568-1568
Open Access | Times Cited: 12
Federated Variational Inference: Towards Improved Personalization and Generalization
Elahe Vedadi, Joshua V. Dillon, P. Mansfield, et al.
Proceedings of the AAAI Symposium Series (2024) Vol. 3, Iss. 1, pp. 323-327
Open Access | Times Cited: 1
FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels
Malte Tölle, Fernando Navarro, Sebastian Eble, et al.
Lecture notes in computer science (2024), pp. 405-415
Closed Access | Times Cited: 1
A review of probabilistic forecasting and prediction with machine learning
Hristos Tyralis, Georgia Papacharalampous
arXiv (Cornell University) (2022)
Open Access | Times Cited: 5
Federated Fuzzy Learning with Imbalanced Data
Lukas Dust, Marina López Murcia, Andreas Makila, et al.
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (2021), pp. 1130-1137
Closed Access | Times Cited: 4
Towards algorithms and models that we can trust: A theoretical perspective
Luca Oneto, Sandro Ridella, Davide Anguita
Neurocomputing (2024) Vol. 592, pp. 127798-127798
Open Access
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune
Chi Xu, Mengen Luo, Erçan E. Kuruoğlu
2022 International Joint Conference on Neural Networks (IJCNN) (2024) Vol. 32, pp. 1-8
Closed Access
New fusion loss function based on knowledge generation using Gumbel-SoftMax for federated learning
Saadat Izadi, Mahmood Ahmadi
The Journal of Supercomputing (2024) Vol. 81, Iss. 1
Closed Access
Data-Driven Automotive Development: Federated Reinforcement Learning for Calibration and Control
Thomas Rudolf, Tobias Schürmann, Matteo Skull, et al.
Proceedings (2022), pp. 369-384
Closed Access | Times Cited: 2
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