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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:

Metalearning: a survey of trends and technologies
Christiane Lemke, Marcin Budka, Bogdan Gabryś
Artificial Intelligence Review (2013) Vol. 44, Iss. 1, pp. 117-130
Open Access | Times Cited: 358

Showing 1-25 of 358 citing articles:

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: 1328
Automated Machine Learning
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
˜The œSpringer series on challenges in machine learning (2019)
Closed Access | Times Cited: 1050
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning
Zechun Liu, Haoyuan Mu, Xiangyu Zhang, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Open Access | Times Cited: 510
Edge-Labeling Graph Neural Network for Few-Shot Learning
Jongmin Kim, Taesup Kim, Sungwoong Kim, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), pp. 11-20
Open Access | Times Cited: 451
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution
Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), pp. 1575-1584
Closed Access | Times Cited: 405
Embracing Change: Continual Learning in Deep Neural Networks
Raia Hadsell, Dushyant Rao, Andrei A. Rusu, et al.
Trends in Cognitive Sciences (2020) Vol. 24, Iss. 12, pp. 1028-1040
Open Access | Times Cited: 320
Ferroelectric ternary content-addressable memory for one-shot learning
Kai Ni, Xunzhao Yin, Ann Franchesca Laguna, et al.
Nature Electronics (2019) Vol. 2, Iss. 11, pp. 521-529
Closed Access | Times Cited: 293
A Review of Android Malware Detection Approaches Based on Machine Learning
Kaijun Liu, Shengwei Xu, Guoai Xu, et al.
IEEE Access (2020) Vol. 8, pp. 124579-124607
Open Access | Times Cited: 278
Meta-Learning
Joaquin Vanschoren
˜The œSpringer series on challenges in machine learning (2019), pp. 35-61
Open Access | Times Cited: 250
Beyond Manual Tuning of Hyperparameters
Frank Hutter, Jörg Lücke, Lars Schmidt-Thieme
KI - Künstliche Intelligenz (2015) Vol. 29, Iss. 4, pp. 329-337
Closed Access | Times Cited: 202
iLearnPlus:a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
Zhen Chen, Pei Zhao, Chen Li, et al.
Nucleic Acids Research (2021) Vol. 49, Iss. 10, pp. e60-e60
Open Access | Times Cited: 197
Taking Human out of Learning Applications: A Survey on Automated Machine Learning
Quanming Yao, Mengshuo Wang, Hugo Jair Escalante, et al.
arXiv (Cornell University) (2018)
Closed Access | Times Cited: 170
Instance Credibility Inference for Few-Shot Learning
Yikai Wang, Chengming Xu, Chen Liu, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Open Access | Times Cited: 164
ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection
Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, et al.
IEEE Transactions on Wireless Communications (2020) Vol. 19, Iss. 5, pp. 3319-3331
Open Access | Times Cited: 144
A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning
Ke Han, Peigang Cao, Yu Wang, et al.
Frontiers in Pharmacology (2022) Vol. 12
Open Access | Times Cited: 100
Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities
Thanh Tung Khuat, Robert Bassett, Ellen Otte, et al.
Computers & Chemical Engineering (2024) Vol. 182, pp. 108585-108585
Open Access | Times Cited: 32
A Meta-Learning Perspective on Cold-Start Recommendations for Items
Manasi Vartak, Arvind Thiagarajan, Conrado Silva Miranda, et al.
Neural Information Processing Systems (2017) Vol. 30, pp. 6904-6914
Closed Access | Times Cited: 140
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Tianqing Zhu, Dayong Ye, Wei Wang, et al.
IEEE Transactions on Knowledge and Data Engineering (2020) Vol. 34, Iss. 6, pp. 2824-2843
Open Access | Times Cited: 138
A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning
Yingguang Li, Changqing Liu, Jiaqi Hua, et al.
CIRP Annals (2019) Vol. 68, Iss. 1, pp. 487-490
Open Access | Times Cited: 118
Clustering algorithm selection by meta-learning systems: A new distance-based problem characterization and ranking combination methods
Daniel G. Ferrari, Leandro Nunes de Castro
Information Sciences (2015) Vol. 301, pp. 181-194
Closed Access | Times Cited: 113
Adaptive and Resilient Soft Tensegrity Robots
John Rieffel, Jean-Baptiste Mouret
Soft Robotics (2018) Vol. 5, Iss. 3, pp. 318-329
Open Access | Times Cited: 108
MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition
Weihe Zhang, Yali Wang, Yu Qiao
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Closed Access | Times Cited: 106
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme
Machine Learning (2017) Vol. 107, Iss. 1, pp. 43-78
Open Access | Times Cited: 101
Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome
Gibran Hemani, Jack Bowden, Philip Haycock, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2017)
Open Access | Times Cited: 90
A review on the self and dual interactions between machine learning and optimisation
Heda Song, Isaac Triguero, Ender Özcan
Progress in Artificial Intelligence (2019) Vol. 8, Iss. 2, pp. 143-165
Open Access | Times Cited: 83
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