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:
Harnessing Prior Knowledge for Explainable Machine Learning: An Overview
Katharina Beckh, Sebastian Müller, Matthias Jakobs, et al.
(2023), pp. 450-463
Closed Access | Times Cited: 9
Katharina Beckh, Sebastian Müller, Matthias Jakobs, et al.
(2023), pp. 450-463
Closed Access | Times Cited: 9
Showing 9 citing articles:
A Survey on Explainable Anomaly Detection
Zhong Li, Yuxuan Zhu, Matthijs van Leeuwen
ACM Transactions on Knowledge Discovery from Data (2023) Vol. 18, Iss. 1, pp. 1-54
Open Access | Times Cited: 41
Zhong Li, Yuxuan Zhu, Matthijs van Leeuwen
ACM Transactions on Knowledge Discovery from Data (2023) Vol. 18, Iss. 1, pp. 1-54
Open Access | Times Cited: 41
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning
Sebastian Müller, Vanessa Toborek, Katharina Beckh, et al.
Lecture notes in computer science (2023), pp. 462-478
Closed Access | Times Cited: 13
Sebastian Müller, Vanessa Toborek, Katharina Beckh, et al.
Lecture notes in computer science (2023), pp. 462-478
Closed Access | Times Cited: 13
From admission to discharge: a systematic review of clinical natural language processing along the patient journey
Katrin Klug, Katharina Beckh, Dario Antweiler, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 3
Katrin Klug, Katharina Beckh, Dario Antweiler, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 3
Towards more sustainable and trustworthy reporting in machine learning
Raphael Fischer, Thomas Liebig, Katharina Morik
Data Mining and Knowledge Discovery (2024) Vol. 38, Iss. 4, pp. 1909-1928
Open Access | Times Cited: 2
Raphael Fischer, Thomas Liebig, Katharina Morik
Data Mining and Knowledge Discovery (2024) Vol. 38, Iss. 4, pp. 1909-1928
Open Access | Times Cited: 2
Sefnet: Linking Tabular Datasets with Semantic Feature Nets
Katarzyna Woźnica, Piotr L. Wilczyński, Przemysław Biecek
(2024)
Closed Access | Times Cited: 1
Katarzyna Woźnica, Piotr L. Wilczyński, Przemysław Biecek
(2024)
Closed Access | Times Cited: 1
Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning
Honghui Wang, Weiming Zhi, Gustavo Batista, et al.
(2024), pp. 15068-15075
Open Access | Times Cited: 1
Honghui Wang, Weiming Zhi, Gustavo Batista, et al.
(2024), pp. 15068-15075
Open Access | Times Cited: 1
AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting
Raphael Fischer, Amal Saadallah
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2024), pp. 806-815
Open Access
Raphael Fischer, Amal Saadallah
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2024), pp. 806-815
Open Access
Enhancing Nonlinear Electrical Circuit Modeling with Prior Knowledge-Infused Neural ODEs
Bernd Zimmering, Jan-Philipp Roche, Oliver Niggemann
2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA) (2024) Vol. 31, pp. 01-08
Closed Access
Bernd Zimmering, Jan-Philipp Roche, Oliver Niggemann
2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA) (2024) Vol. 31, pp. 01-08
Closed Access
Explainable Vertical Federated Learning for Healthcare: Ensuring Privacy and Optimal Accuracy
Shahnewaz Karim Sakib, Anindya Bijoy Das
2021 IEEE International Conference on Big Data (Big Data) (2024), pp. 5068-5077
Closed Access
Shahnewaz Karim Sakib, Anindya Bijoy Das
2021 IEEE International Conference on Big Data (Big Data) (2024), pp. 5068-5077
Closed Access
