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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:

Solving Rubik's Cube with a Robot Hand
OpenAI, Ilge Akkaya, Marcin Andrychowicz, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 655

Showing 1-25 of 655 citing articles:

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: 1524
Dota 2 with Large Scale Deep Reinforcement Learning
Christopher Berner, Greg Brockman, Brooke Chan, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 1019
Learning quadrupedal locomotion over challenging terrain
Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, et al.
Science Robotics (2020) Vol. 5, Iss. 47
Open Access | Times Cited: 725
Toward Causal Representation Learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, et al.
Proceedings of the IEEE (2021) Vol. 109, Iss. 5, pp. 612-634
Open Access | Times Cited: 631
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, et al.
Nature Machine Intelligence (2020) Vol. 2, Iss. 11, pp. 665-673
Closed Access | Times Cited: 484
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
Guoguang Du, Kai Wang, Shiguo Lian, et al.
Artificial Intelligence Review (2020) Vol. 54, Iss. 3, pp. 1677-1734
Open Access | Times Cited: 351
Multi-Task Learning with Deep Neural Networks: A Survey
Michael Crawshaw
arXiv (Cornell University) (2020)
Open Access | Times Cited: 345
The unreasonable effectiveness of deep learning in artificial intelligence
Terrence J. Sejnowski
Proceedings of the National Academy of Sciences (2020) Vol. 117, Iss. 48, pp. 30033-30038
Open Access | Times Cited: 319
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 269
Deep learning, reinforcement learning, and world models
Yutaka Matsuo, Yann LeCun, Maneesh Sahani, et al.
Neural Networks (2022) Vol. 152, pp. 267-275
Open Access | Times Cited: 259
Reinforcement Learning with Augmented Data
Michael Laskin, Kimin Lee, Adam Stooke, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 249
The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
Gary Marcus
arXiv (Cornell University) (2020)
Open Access | Times Cited: 229
A Survey on Learning-Based Robotic Grasping
Kilian Kleeberger, Richard Bormann, Werner Kraus, et al.
Current Robotics Reports (2020) Vol. 1, Iss. 4, pp. 239-249
Open Access | Times Cited: 208
Deep Reinforcement Learning and Its Neuroscientific Implications
Matthew Botvinick, Jane X. Wang, Will Dabney, et al.
Neuron (2020) Vol. 107, Iss. 4, pp. 603-616
Open Access | Times Cited: 202
A Review of Physics Simulators for Robotic Applications
Jack Collins, Shelvin Chand, Anthony Vanderkop, et al.
IEEE Access (2021) Vol. 9, pp. 51416-51431
Open Access | Times Cited: 170
Machine Learning for Soft Robotic Sensing and Control
Keene Chin, Tess Hellebrekers, Carmel Majidi
Advanced Intelligent Systems (2020) Vol. 2, Iss. 6
Open Access | Times Cited: 164
TACTO: A Fast, Flexible, and Open-Source Simulator for High-Resolution Vision-Based Tactile Sensors
Shaoxiong Wang, Mike Lambeta, Po-Wei Chou, et al.
IEEE Robotics and Automation Letters (2022) Vol. 7, Iss. 2, pp. 3930-3937
Open Access | Times Cited: 85
Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
Kedar Hippalgaonkar, Qianxiao Li, Xiaonan Wang, et al.
Nature Reviews Materials (2023) Vol. 8, Iss. 4, pp. 241-260
Closed Access | Times Cited: 84
Rapid Locomotion via Reinforcement Learning
Gabriel B. Margolis, Ge Yang, Kartik Paigwar, et al.
(2022)
Open Access | Times Cited: 76
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning
Robert Kirk, Amy Zhang, Edward Grefenstette, et al.
Journal of Artificial Intelligence Research (2023) Vol. 76, pp. 201-264
Open Access | Times Cited: 72
Parallel Learning: Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real
Qinghai Miao, Yisheng Lv, Min Huang, et al.
IEEE/CAA Journal of Automatica Sinica (2023) Vol. 10, Iss. 3, pp. 603-631
Closed Access | Times Cited: 54
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
Ankur Handa, Arthur Allshire, Viktor Makoviychuk, et al.
(2023)
Open Access | Times Cited: 45
Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions
Laura von Rueden, Sebastian Mayer, Rafet Sifa, et al.
Lecture notes in computer science (2020), pp. 548-560
Closed Access | Times Cited: 109
Deep Learning in Robotics: Survey on Model Structures and Training Strategies
Artúr I. Károly, Péter Galambos, József Kuti, et al.
IEEE Transactions on Systems Man and Cybernetics Systems (2020) Vol. 51, Iss. 1, pp. 266-279
Open Access | Times Cited: 106
Next-generation deep learning based on simulators and synthetic data
Celso M. de Melo, Antonio Torralba, Leonidas Guibas, et al.
Trends in Cognitive Sciences (2021) Vol. 26, Iss. 2, pp. 174-187
Closed Access | Times Cited: 99
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