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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:

Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, et al.
Proceedings of the National Academy of Sciences (2017) Vol. 114, Iss. 13, pp. 3521-3526
Open Access | Times Cited: 5292

Showing 1-25 of 5292 citing articles:

Learning without Forgetting
Zhizhong Li, Derek Hoiem
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017) Vol. 40, Iss. 12, pp. 2935-2947
Open Access | Times Cited: 2893
iCaRL: Incremental Classifier and Representation Learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, et al.
(2017), pp. 5533-5542
Closed Access | Times Cited: 2800
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: 2461
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Roberto Cipolla, Yarin Gal, Alex Kendall
(2018), pp. 7482-7491
Open Access | Times Cited: 2326
Generalizing from a Few Examples
Yaqing Wang, Quanming Yao, James T. Kwok, et al.
ACM Computing Surveys (2020) Vol. 53, Iss. 3, pp. 1-34
Closed Access | Times Cited: 1900
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: 1539
The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms
Spyros Makridakis
Futures (2017) Vol. 90, pp. 46-60
Closed Access | Times Cited: 1391
Neuroscience-Inspired Artificial Intelligence
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, et al.
Neuron (2017) Vol. 95, Iss. 2, pp. 245-258
Open Access | Times Cited: 1334
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: 1145
FiLM: Visual Reasoning with a General Conditioning Layer
Ethan Perez, Florian Strub, Harm de Vries, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2018) Vol. 32, Iss. 1
Open Access | Times Cited: 1029
Large Scale Incremental Learning
Yue Wu, Yinpeng Chen, Lijuan Wang, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Open Access | Times Cited: 983
End-to-End Incremental Learning
Francisco M. Castro, Manuel J. Marín‐Jiménez, Nicolás Guil, et al.
Lecture notes in computer science (2018), pp. 241-257
Open Access | Times Cited: 972
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
Arun Mallya, Svetlana Lazebnik
(2018)
Open Access | Times Cited: 961
Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications
Thanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi
IEEE Transactions on Cybernetics (2020) Vol. 50, Iss. 9, pp. 3826-3839
Open Access | Times Cited: 898
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: 876
What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior
Timothy E.J. Behrens, Timothy Müller, James C. R. Whittington, et al.
Neuron (2018) Vol. 100, Iss. 2, pp. 490-509
Open Access | Times Cited: 872
Physics for neuromorphic computing
Danijela Marković, Alice Mizrahi, Damien Querlioz, et al.
Nature Reviews Physics (2020) Vol. 2, Iss. 9, pp. 499-510
Open Access | Times Cited: 747
How To Backdoor Federated Learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, et al.
arXiv (Cornell University) (2018)
Open Access | Times Cited: 717
Generalizing from a Few Examples: A Survey on Few-Shot Learning
Yaqing Wang, Quanming Yao, James T. Kwok, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 658
UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory
Iasonas Kokkinos
(2017), pp. 5454-5463
Open Access | Times Cited: 658
Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges
Jianshi Tang, Fang Yuan, Xinke Shen, et al.
Advanced Materials (2019) Vol. 31, Iss. 49
Closed Access | Times Cited: 611
Designing neural networks through neuroevolution
Kenneth O. Stanley, Jeff Clune, Joel Lehman, et al.
Nature Machine Intelligence (2018) Vol. 1, Iss. 1, pp. 24-35
Closed Access | Times Cited: 606
Sentiment Analysis: Mining Opinions, Sentiments, and Emotions
Bing Liu
(2015)
Closed Access | Times Cited: 584
Federated learning on non-IID data: A survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu, et al.
Neurocomputing (2021) Vol. 465, pp. 371-390
Open Access | Times Cited: 548
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi
Neural Information Processing Systems (2017) Vol. 30, pp. 506-516
Closed Access | Times Cited: 532
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