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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:

Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
Youngseog Chung, Ian Char, Han Guo, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

Uncertainty quantification by direct propagation of shallow ensembles
Matthias Kellner, Michele Ceriotti
Machine Learning Science and Technology (2024) Vol. 5, Iss. 3, pp. 035006-035006
Open Access | Times Cited: 9
Camera, LiDAR, and Radar Sensor Fusion Based on Bayesian Neural Network (CLR-BNN)
Ratheesh Ravindran, Michael Santora, Mohsin M. Jamali
IEEE Sensors Journal (2022) Vol. 22, Iss. 7, pp. 6964-6974
Closed Access | Times Cited: 34
Uncertainty quantification for molecular property predictions with graph neural architecture search
Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, et al.
Digital Discovery (2024) Vol. 3, Iss. 8, pp. 1534-1553
Open Access | Times Cited: 6
High-dimensional Bayesian optimization of 23 hyperparameters over 100 iterations for an attention-based network to predict materials property: A case study on CrabNet using Ax platform and SAASBO
Sterling G. Baird, Marianne Liu, Taylor D. Sparks
Computational Materials Science (2022) Vol. 211, pp. 111505-111505
Open Access | Times Cited: 22
A benchmark on uncertainty quantification for deep learning prognostics
Luis Basora, Arthur Viens, Manuel Arias Chao, et al.
Reliability Engineering & System Safety (2024) Vol. 253, pp. 110513-110513
Open Access | Times Cited: 4
A hybrid probabilistic battery health management approach for robust inspection drone operations
Jokin Alcibar, Jose Ignacio Aizpurua, Ekhi Zugasti, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 146, pp. 110246-110246
Closed Access
Prediction uncertainty validation for computational chemists
Pascal Pernot
The Journal of Chemical Physics (2022) Vol. 157, Iss. 14
Open Access | Times Cited: 17
Tropical Cyclone Intensity Probabilistic Forecasting System Based on Deep Learning
Fan Meng, Kun‐Lin Yang, Yichen Yao, et al.
International Journal of Intelligent Systems (2023) Vol. 2023, pp. 1-17
Open Access | Times Cited: 9
Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification
Cameron Gruich, Varun Madhavan, Zhaoran Wang, et al.
Machine Learning Science and Technology (2023) Vol. 4, Iss. 2, pp. 025019-025019
Open Access | Times Cited: 9
Probabilistic forecasting of tropical cyclones intensity using machine learning model
Meng Fan, Yi-Chen Yao, Zhibin Wang, et al.
Environmental Research Letters (2023) Vol. 18, Iss. 4, pp. 044042-044042
Open Access | Times Cited: 8
Uncertainty-Aware Learning with Label Noise for Glacier Mass Balance Modelling
Codruţ-Andrei Diaconu, Nina Maria Gottschling
IEEE Geoscience and Remote Sensing Letters (2024) Vol. 21, pp. 1-5
Open Access | Times Cited: 2
Pretrain, Prompt, and Transfer: Evolving Digital Twins for Time-to-Event Analysis in Cyber-Physical Systems
Qinghua Xu, Tao Yue, Shaukat Ali, et al.
IEEE Transactions on Software Engineering (2024) Vol. 50, Iss. 6, pp. 1464-1477
Open Access | Times Cited: 2
Early Prediction of Knee Point and Knee Capacity for Fast-Charging Lithium-Ion Battery With Uncertainty Quantification and Calibration
Yuqi Ke, Yiyue Jiang, Rong Zhu, et al.
IEEE Transactions on Transportation Electrification (2023) Vol. 10, Iss. 2, pp. 2873-2885
Closed Access | Times Cited: 6
Uncertainty Quantification for Deep Learning-Based Remote Photoplethysmography
Rencheng Song, Han Wang, Haojie Xia, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-12
Closed Access | Times Cited: 5
UNIQUE: A Framework for Uncertainty Quantification Benchmarking
Jessica Lanini, Minh Huynh, Gaetano Scebba, et al.
(2024)
Open Access | Times Cited: 1
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
Steven Goldenberg, Malachi Schram, Kishansingh Rajput, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 4, pp. 045009-045009
Open Access | Times Cited: 1
A data-driven uncertainty quantification framework in probabilistic bio-inspired porous materials (Material-UQ): An investigation for RotTMPS plates
Duong Q. Nguyen, Kim Q. Tran, Thinh D. Le, et al.
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 435, pp. 117603-117603
Closed Access | Times Cited: 1
Evolve the Model Universe of a System Universe
Tao Yue, Shaukat Ali
2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) (2023), pp. 1726-1731
Open Access | Times Cited: 3
Genetic-evolutionary adaptive moment estimation-based semisupervised deep sequential convolution network for seismic impedance inversion: Application and uncertainty analysis
Anjali Dixit, Animesh Mandal, Shib Sankar Ganguli, et al.
Geophysics (2022) Vol. 88, Iss. 2, pp. R225-R242
Closed Access | Times Cited: 5
The role amenities play in spatial sorting of migrants and their impact on welfare: Evidence from China
Yunda Zhang
PLoS ONE (2023) Vol. 18, Iss. 2, pp. e0281669-e0281669
Open Access | Times Cited: 2
Probabilistic and Physics-Informed Machine Learning for Predictive Maintenance with Time Series Data
Phan-Anh Vu, Emanuel Aldea, Mounira Bouarroudj, et al.
(2023), pp. 1-8
Closed Access | Times Cited: 2
Multi-module-based CVAE to predict HVCM faults in the SNS accelerator
Yasir Alanazi, Malachi Schram, Kishansingh Rajput, et al.
Machine Learning with Applications (2023) Vol. 13, pp. 100484-100484
Open Access | Times Cited: 2
Meta-learning to calibrate Gaussian processes with deep kernels for regression uncertainty estimation
Tomoharu Iwata, Atsutoshi Kumagai
Neurocomputing (2024) Vol. 579, pp. 127441-127441
Open Access
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
Tailin Wu, Willie Neiswanger, Hongtao Zheng, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 1, pp. 320-328
Open Access
Towards a Probabilistic Fusion Approach for Robust Battery Prognostics
Jokin Alcibar, Jose Ignacio Aizpurua, Ekhi Zugasti
PHM Society European Conference (2024) Vol. 8, Iss. 1, pp. 13-13
Open Access
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