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:
How to Evaluate Uncertainty Estimates in Machine Learning for Regression?
Laurens Sluijterman, Eric Cator, Tom Heskes
arXiv (Cornell University) (2021)
Open Access | Times Cited: 5
Laurens Sluijterman, Eric Cator, Tom Heskes
arXiv (Cornell University) (2021)
Open Access | Times Cited: 5
Showing 5 citing articles:
Uncertainty Quantification and Interval Prediction of Equipment Remaining Useful Life Based on Semisupervised Learning
Hui Liu, Zhenyu Liu, Donghao Zhang, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 73, pp. 1-15
Closed Access | Times Cited: 8
Hui Liu, Zhenyu Liu, Donghao Zhang, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 73, pp. 1-15
Closed Access | Times Cited: 8
Analytical Uncertainty Propagation in Neural Networks
Paul Jungmann, Julia Poray, Akash Kumar
IEEE Transactions on Neural Networks and Learning Systems (2024) Vol. 36, Iss. 2, pp. 2495-2508
Closed Access | Times Cited: 1
Paul Jungmann, Julia Poray, Akash Kumar
IEEE Transactions on Neural Networks and Learning Systems (2024) Vol. 36, Iss. 2, pp. 2495-2508
Closed Access | Times Cited: 1
Identifying the validity domain of machine learning models in building energy systems
Martin Rätz, Patrick Henkel, Phillip Stoffel, et al.
Energy and AI (2023) Vol. 15, pp. 100324-100324
Open Access | Times Cited: 2
Martin Rätz, Patrick Henkel, Phillip Stoffel, et al.
Energy and AI (2023) Vol. 15, pp. 100324-100324
Open Access | Times Cited: 2
Uncertainty-aware Evaluation of Machine Learning Performance in binary Classification Tasks
Leo Sperling, Simon Lämmer, Hans Hagen, et al.
Journal of WSCG (2022) Vol. 30, Iss. 1-2, pp. 63-71
Open Access | Times Cited: 3
Leo Sperling, Simon Lämmer, Hans Hagen, et al.
Journal of WSCG (2022) Vol. 30, Iss. 1-2, pp. 63-71
Open Access | Times Cited: 3
The Effect of Training Data Quantity on Monte Carlo Dropout Uncertainty Quantification in Deep Learning
Harrison Cusack, Alina Bialkowski
2022 International Joint Conference on Neural Networks (IJCNN) (2023), pp. 1-8
Closed Access | Times Cited: 1
Harrison Cusack, Alina Bialkowski
2022 International Joint Conference on Neural Networks (IJCNN) (2023), pp. 1-8
Closed Access | Times Cited: 1
