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:
Design of the 2015 ChaLearn AutoML challenge
Isabelle Guyon, Kristin P. Bennett, Gavin C. Cawley, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2015), pp. 1-8
Closed Access | Times Cited: 121
Isabelle Guyon, Kristin P. Bennett, Gavin C. Cawley, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2015), pp. 1-8
Closed Access | Times Cited: 121
Showing 1-25 of 121 citing articles:
Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, et al.
Neural Information Processing Systems (2015) Vol. 28, pp. 2755-2763
Closed Access | Times Cited: 1126
Matthias Feurer, Aaron Klein, Katharina Eggensperger, et al.
Neural Information Processing Systems (2015) Vol. 28, pp. 2755-2763
Closed Access | Times Cited: 1126
Automated Machine Learning
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
The Springer series on challenges in machine learning (2019)
Closed Access | Times Cited: 1024
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
The Springer series on challenges in machine learning (2019)
Closed Access | Times Cited: 1024
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Jonathan Waring, Charlotta Lindvall, Renato Umeton
Artificial Intelligence in Medicine (2020) Vol. 104, pp. 101822-101822
Open Access | Times Cited: 661
Jonathan Waring, Charlotta Lindvall, Renato Umeton
Artificial Intelligence in Medicine (2020) Vol. 104, pp. 101822-101822
Open Access | Times Cited: 661
Auto-sklearn: Efficient and Robust Automated Machine Learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, et al.
The Springer series on challenges in machine learning (2019), pp. 113-134
Open Access | Times Cited: 333
Matthias Feurer, Aaron Klein, Katharina Eggensperger, et al.
The Springer series on challenges in machine learning (2019), pp. 113-134
Open Access | Times Cited: 333
A review of automatic selection methods for machine learning algorithms and hyper-parameter values
Gang Luo
Network Modeling Analysis in Health Informatics and Bioinformatics (2016) Vol. 5, Iss. 1
Closed Access | Times Cited: 325
Gang Luo
Network Modeling Analysis in Health Informatics and Bioinformatics (2016) Vol. 5, Iss. 1
Closed Access | Times Cited: 325
Benchmark and Survey of Automated Machine Learning Frameworks
Marc-André Zöller, Marco F. Huber
Journal of Artificial Intelligence Research (2021) Vol. 70, pp. 409-472
Open Access | Times Cited: 307
Marc-André Zöller, Marco F. Huber
Journal of Artificial Intelligence Research (2021) Vol. 70, pp. 409-472
Open Access | Times Cited: 307
Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation
Ioannis Tsamardinos, Elissavet Greasidou, Giorgos Borboudakis
Machine Learning (2018) Vol. 107, Iss. 12, pp. 1895-1922
Open Access | Times Cited: 163
Ioannis Tsamardinos, Elissavet Greasidou, Giorgos Borboudakis
Machine Learning (2018) Vol. 107, Iss. 12, pp. 1895-1922
Open Access | Times Cited: 163
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
Lucas Zimmer, Marius Lindauer, Frank Hutter
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Vol. 43, Iss. 9, pp. 3079-3090
Closed Access | Times Cited: 146
Lucas Zimmer, Marius Lindauer, Frank Hutter
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Vol. 43, Iss. 9, pp. 3079-3090
Closed Access | Times Cited: 146
Automated Machine Learning for Healthcare and Clinical Notes Analysis
Akram Mustafa, Mostafa Rahimi Azghadi
Computers (2021) Vol. 10, Iss. 2, pp. 24-24
Open Access | Times Cited: 101
Akram Mustafa, Mostafa Rahimi Azghadi
Computers (2021) Vol. 10, Iss. 2, pp. 24-24
Open Access | Times Cited: 101
The roles of supervised machine learning in systems neuroscience
Joshua I. Glaser, Ari S. Benjamin, Roozbeh Farhoodi, et al.
Progress in Neurobiology (2019) Vol. 175, pp. 126-137
Open Access | Times Cited: 129
Joshua I. Glaser, Ari S. Benjamin, Roozbeh Farhoodi, et al.
Progress in Neurobiology (2019) Vol. 175, pp. 126-137
Open Access | Times Cited: 129
DENSER: deep evolutionary network structured representation
Filipe Assunção, Nuno Lourenço, Penousal Machado, et al.
Genetic Programming and Evolvable Machines (2018) Vol. 20, Iss. 1, pp. 5-35
Closed Access | Times Cited: 122
Filipe Assunção, Nuno Lourenço, Penousal Machado, et al.
Genetic Programming and Evolvable Machines (2018) Vol. 20, Iss. 1, pp. 5-35
Closed Access | Times Cited: 122
Analysis of the AutoML Challenge Series 2015–2018
Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, et al.
The Springer series on challenges in machine learning (2019), pp. 177-219
Closed Access | Times Cited: 113
Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, et al.
The Springer series on challenges in machine learning (2019), pp. 177-219
Closed Access | Times Cited: 113
Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data
Andreas Holzinger, Benjamin Haibe‐Kains, Igor Jurišica
European Journal of Nuclear Medicine and Molecular Imaging (2019) Vol. 46, Iss. 13, pp. 2722-2730
Closed Access | Times Cited: 103
Andreas Holzinger, Benjamin Haibe‐Kains, Igor Jurišica
European Journal of Nuclear Medicine and Molecular Imaging (2019) Vol. 46, Iss. 13, pp. 2722-2730
Closed Access | Times Cited: 103
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: 82
Heda Song, Isaac Triguero, Ender Özcan
Progress in Artificial Intelligence (2019) Vol. 8, Iss. 2, pp. 143-165
Open Access | Times Cited: 82
Automated Machine Learning—A Brief Review at the End of the Early Years
Hugo Jair Escalante
Natural computing series (2021), pp. 11-28
Closed Access | Times Cited: 73
Hugo Jair Escalante
Natural computing series (2021), pp. 11-28
Closed Access | Times Cited: 73
Auto-Sklearn 2.0: The Next Generation
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 70
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 70
Assessing the new Natural Resources Conservation Service water supply forecast model for the American West: A challenging test of explainable, automated, ensemble artificial intelligence
Sean W. Fleming, D. C. Garen, Angus G. Goodbody, et al.
Journal of Hydrology (2021) Vol. 602, pp. 126782-126782
Open Access | Times Cited: 57
Sean W. Fleming, D. C. Garen, Angus G. Goodbody, et al.
Journal of Hydrology (2021) Vol. 602, pp. 126782-126782
Open Access | Times Cited: 57
AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network
Abhilash Singh, J. Amutha, Jaiprakash Nagar, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 49
Abhilash Singh, J. Amutha, Jaiprakash Nagar, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 49
Eight years of AutoML: categorisation, review and trends
Rafael Barbudo, Sebastián Ventura, José Raúl Romero
Knowledge and Information Systems (2023) Vol. 65, Iss. 12, pp. 5097-5149
Open Access | Times Cited: 24
Rafael Barbudo, Sebastián Ventura, José Raúl Romero
Knowledge and Information Systems (2023) Vol. 65, Iss. 12, pp. 5097-5149
Open Access | Times Cited: 24
Towards Automatically-Tuned Deep Neural Networks
Hector Mendoza, Aaron Klein, Matthias Feurer, et al.
The Springer series on challenges in machine learning (2019), pp. 135-149
Open Access | Times Cited: 63
Hector Mendoza, Aaron Klein, Matthias Feurer, et al.
The Springer series on challenges in machine learning (2019), pp. 135-149
Open Access | Times Cited: 63
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Linnan Wang, Yiyang Zhao, Yuu Jinnai, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 61
Linnan Wang, Yiyang Zhao, Yuu Jinnai, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 61
A Review on Automated Machine Learning (AutoML) Systems
Thiloshon Nagarajah, Guhanathan Poravi
2022 IEEE 7th International conference for Convergence in Technology (I2CT) (2019), pp. 1-6
Closed Access | Times Cited: 60
Thiloshon Nagarajah, Guhanathan Poravi
2022 IEEE 7th International conference for Convergence in Technology (I2CT) (2019), pp. 1-6
Closed Access | Times Cited: 60
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
Micah J. Smith, Carles Sala, James Max Kanter, et al.
(2020), pp. 785-800
Open Access | Times Cited: 51
Micah J. Smith, Carles Sala, James Max Kanter, et al.
(2020), pp. 785-800
Open Access | Times Cited: 51
Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium
Li Cai, Lei Ren, Yongheng Wang, et al.
Royal Society Open Science (2021) Vol. 8, Iss. 1, pp. 201121-201121
Open Access | Times Cited: 50
Li Cai, Lei Ren, Yongheng Wang, et al.
Royal Society Open Science (2021) Vol. 8, Iss. 1, pp. 201121-201121
Open Access | Times Cited: 50
Transfer learning-assisted 3D deep learning models for knee osteoarthritis detection: Data from the osteoarthritis initiative
Pauline Shan Qing Yeoh, Khin Wee Lai, Siew Li Goh, et al.
Frontiers in Bioengineering and Biotechnology (2023) Vol. 11
Open Access | Times Cited: 16
Pauline Shan Qing Yeoh, Khin Wee Lai, Siew Li Goh, et al.
Frontiers in Bioengineering and Biotechnology (2023) Vol. 11
Open Access | Times Cited: 16
