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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:

Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Gabriel Erion, Joseph D. Janizek, Pascal Sturmfels, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 7, pp. 620-631
Open Access | Times Cited: 141

Showing 1-25 of 141 citing articles:

AI for radiographic COVID-19 detection selects shortcuts over signal
Alex J. DeGrave, Joseph D. Janizek, Su‐In Lee
Nature Machine Intelligence (2021) Vol. 3, Iss. 7, pp. 610-619
Open Access | Times Cited: 341
Transparency of deep neural networks for medical image analysis: A review of interpretability methods
Zohaib Salahuddin, Henry C. Woodruff, Avishek Chatterjee, et al.
Computers in Biology and Medicine (2021) Vol. 140, pp. 105111-105111
Open Access | Times Cited: 280
Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments
Shijie Jiang, Yi Zheng, Chao Wang, et al.
Water Resources Research (2021) Vol. 58, Iss. 1
Closed Access | Times Cited: 155
COVID-19 image classification using deep learning: Advances, challenges and opportunities
Priya Aggarwal, Narendra Kumar Mishra, Binish Fatimah, et al.
Computers in Biology and Medicine (2022) Vol. 144, pp. 105350-105350
Open Access | Times Cited: 116
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning
Hayden Gunraj, Ali Sabri, David Koff, et al.
Frontiers in Medicine (2022) Vol. 8
Open Access | Times Cited: 100
Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions
Kumar Puran Tripathy, Ashok K. Mishra
Journal of Hydrology (2023) Vol. 628, pp. 130458-130458
Closed Access | Times Cited: 81
Beyond explaining: Opportunities and challenges of XAI-based model improvement
Leander Weber, Sebastian Lapuschkin, Alexander Binder, et al.
Information Fusion (2022) Vol. 92, pp. 154-176
Open Access | Times Cited: 73
Interpretable and explainable machine learning: A methods‐centric overview with concrete examples
Ričards Marcinkevičs, Julia E. Vogt
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2023) Vol. 13, Iss. 3
Open Access | Times Cited: 68
Deep learning for water quality
Wei Zhi, Alison P. Appling, Heather E. Golden, et al.
Nature Water (2024) Vol. 2, Iss. 3, pp. 228-241
Closed Access | Times Cited: 58
Shapley value: from cooperative game to explainable artificial intelligence
Meng Li, Hengyang Sun, Yanjun Huang, et al.
Autonomous Intelligent Systems (2024) Vol. 4, Iss. 1
Open Access | Times Cited: 19
Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon
Chao Wang, Shijie Jiang, Yi Zheng, et al.
Water Resources Research (2024) Vol. 60, Iss. 4
Open Access | Times Cited: 18
Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions
Wenxin Xu, Jie Chen, Gerald Corzo, et al.
Water Resources Research (2024) Vol. 60, Iss. 2
Open Access | Times Cited: 17
A Deep Convolutional Neural Network Method to Detect Seizures and Characteristic Frequencies Using Epileptic Electroencephalogram (EEG) Data
Md. Rashed-Al-Mahfuz, Mohammad Ali Moni, Shahadat Uddin, et al.
IEEE Journal of Translational Engineering in Health and Medicine (2021) Vol. 9, pp. 1-12
Open Access | Times Cited: 92
Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective
Simon Letzgus, Patrick Wagner, Jonas Lederer, et al.
IEEE Signal Processing Magazine (2022) Vol. 39, Iss. 4, pp. 40-58
Open Access | Times Cited: 62
Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method
Hejiang Cai, Suning Liu, Haiyun Shi, et al.
Journal of Hydrology (2022) Vol. 613, pp. 128495-128495
Closed Access | Times Cited: 62
Diverse partial reprogramming strategies restore youthful gene expression and transiently suppress cell identity
Antoine E. Roux, Chunlian Zhang, Jonathan S. Paw, et al.
Cell Systems (2022) Vol. 13, Iss. 7, pp. 574-587.e11
Open Access | Times Cited: 46
Data-driven multi-step prediction and analysis of monthly rainfall using explainable deep learning
Renfei He, Limao Zhang, Alvin Wei Ze Chew
Expert Systems with Applications (2023) Vol. 235, pp. 121160-121160
Closed Access | Times Cited: 38
Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection
Chia-Yu Hsu, Wenwen Li
International Journal of Geographical Information Science (2023) Vol. 37, Iss. 5, pp. 963-987
Open Access | Times Cited: 30
Machine learning in solar physics
A. Asensio Ramos, Mark C. M. Cheung, Iulia Chifu, et al.
Living Reviews in Solar Physics (2023) Vol. 20, Iss. 1
Open Access | Times Cited: 30
Correcting gradient-based interpretations of deep neural networks for genomics
Antonio Majdandzic, Chandana Rajesh, Peter K. Koo
Genome biology (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 28
Real-time detection and analysis of foodborne pathogens via machine learning based fiber-optic Raman sensor
Bohong Zhang, Md Asad Rahman, Jinling Liu, et al.
Measurement (2023) Vol. 217, pp. 113121-113121
Open Access | Times Cited: 26
Towards white box modeling of compressive strength of sustainable ternary cement concrete using explainable artificial intelligence (XAI)
Syed Muhammad Ibrahim, Saad Shamim Ansari, Syed Danish Hasan
Applied Soft Computing (2023) Vol. 149, pp. 110997-110997
Closed Access | Times Cited: 23
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning
Yuyang Gao, Siyi Gu, Junji Jiang, et al.
ACM Computing Surveys (2024) Vol. 56, Iss. 7, pp. 1-39
Open Access | Times Cited: 11
Toward interpretable LSTM-based modeling of hydrological systems
Luis De La Fuente, Mohammad Reza Ehsani, Hoshin V. Gupta, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 4, pp. 945-971
Open Access | Times Cited: 10
Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model
Hejiang Cai, Haiyun Shi, Zhaoqiang Zhou, et al.
Water Resources Research (2024) Vol. 60, Iss. 7
Open Access | Times Cited: 9
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