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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:

Continual lifelong learning with neural networks: A review
German I. Parisi, Ronald Kemker, Jose L. Part, et al.
Neural Networks (2019) Vol. 113, pp. 54-71
Open Access | Times Cited: 2479

Showing 1-25 of 2479 citing articles:

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, et al.
Journal Of Big Data (2021) Vol. 8, Iss. 1
Open Access | Times Cited: 4887
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1565
A Survey on Multi-Task Learning
Yu Zhang, Qiang Yang
IEEE Transactions on Knowledge and Data Engineering (2021) Vol. 34, Iss. 12, pp. 5586-5609
Open Access | Times Cited: 1378
Meta-Learning in Neural Networks: A Survey
Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021), pp. 1-1
Open Access | Times Cited: 1341
A continual learning survey: Defying forgetting in classification tasks
Matthias Delange, Rahaf Aljundi, Marc Masana, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021), pp. 1-1
Open Access | Times Cited: 1151
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
Di Feng, Christian Schütz, Lars Rosenbaum, et al.
IEEE Transactions on Intelligent Transportation Systems (2020) Vol. 22, Iss. 3, pp. 1341-1360
Open Access | Times Cited: 990
Artificial intelligence: A powerful paradigm for scientific research
Yongjun Xu, Xin Liu, Xin Cao, et al.
The Innovation (2021) Vol. 2, Iss. 4, pp. 100179-100179
Open Access | Times Cited: 967
Learning a Unified Classifier Incrementally via Rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Closed Access | Times Cited: 881
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Sebastian Raisch, Sebastian Krakowski
Academy of Management Review (2021) Vol. 46, Iss. 1, pp. 192-210
Open Access | Times Cited: 879
A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development
Tianyu Wu, Shizhu He, Jingping Liu, et al.
IEEE/CAA Journal of Automatica Sinica (2023) Vol. 10, Iss. 5, pp. 1122-1136
Closed Access | Times Cited: 758
Conditional Prompt Learning for Vision-Language Models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
Open Access | Times Cited: 695
A Survey on Multi-Task Learning
Yu Zhang, Qiang Yang
arXiv (Cornell University) (2017)
Open Access | Times Cited: 678
ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding
Yu Sun, Shuohuan Wang, Yukun Li, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2020) Vol. 34, Iss. 05, pp. 8968-8975
Open Access | Times Cited: 652
Three scenarios for continual learning
Gido M. van de Ven, Andreas S. Tolias
arXiv (Cornell University) (2019)
Open Access | Times Cited: 493
A survey on active learning and human-in-the-loop deep learning for medical image analysis
Samuel Budd, Emma C. Robinson, Bernhard Kainz
Medical Image Analysis (2021) Vol. 71, pp. 102062-102062
Open Access | Times Cited: 441
Deep learning for tomographic image reconstruction
Ge Wang, Jong Chul Ye, Bruno De Man
Nature Machine Intelligence (2020) Vol. 2, Iss. 12, pp. 737-748
Closed Access | Times Cited: 410
Can deep learning beat numerical weather prediction?
Martin G. Schultz, Clara Betancourt, Bing Gong, et al.
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2021) Vol. 379, Iss. 2194, pp. 20200097-20200097
Open Access | Times Cited: 396
Online learning: A comprehensive survey
Steven C. H. Hoi, Doyen Sahoo, Jing Lu, et al.
Neurocomputing (2021) Vol. 459, pp. 249-289
Open Access | Times Cited: 388
Big data analytics for intelligent manufacturing systems: A review
Junliang Wang, Chuqiao Xu, Jie Zhang, et al.
Journal of Manufacturing Systems (2021) Vol. 62, pp. 738-752
Closed Access | Times Cited: 379
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, et al.
Information Fusion (2019) Vol. 58, pp. 52-68
Open Access | Times Cited: 369
Class-Incremental Learning: Survey and Performance Evaluation on Image Classification
Marc Masana, Xialei Liu, Bartłomiej Twardowski, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2022) Vol. 45, Iss. 5, pp. 5513-5533
Open Access | Times Cited: 356
Multi-Task Learning with Deep Neural Networks: A Survey
Michael Crawshaw
arXiv (Cornell University) (2020)
Open Access | Times Cited: 350
Experience Replay for Continual Learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, et al.
arXiv (Cornell University) (2018)
Open Access | Times Cited: 348
Learning to Prompt for Continual Learning
Zifeng Wang, Zizhao Zhang, Chen‐Yu Lee, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 139-149
Open Access | Times Cited: 344
If deep learning is the answer, what is the question?
Andrew Saxe, Stephanie Nelli, Christopher Summerfield
Nature reviews. Neuroscience (2020) Vol. 22, Iss. 1, pp. 55-67
Open Access | Times Cited: 343
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