Institution · OpenAI (United States)
Most cited papers from OpenAI (United States)
What are the most cited papers from OpenAI (United States)?
The most cited paper from OpenAI (United States) is "ImageNet classification with deep convolutional neural networks" (Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, 2017), with 75,634 citations. Next come "Deep Learning with Differential Privacy" (6,312) and "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" (5,777).
Showing 50 of 56 papers. Every paper with more than 130 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | ImageNet classification with deep convolutional neural networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · Communications of the ACM | 24 May 2017 | 75,634 |
| 2 | Deep Learning with Differential Privacy Martı́n Abadi, Andy Chu, Ian Goodfellow et al. | 24 Oct 2016 | 6,312 |
| 3 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn, Pieter Abbeel, Sergey Levine · arXiv (Cornell University) | 9 Mar 2017 | 5,777 |
| 4 | Learning Transferable Visual Models From Natural Language Supervision Alec Radford, Jong Wook Kim, Chris Hallacy et al. · arXiv (Cornell University) | 26 Feb 2021 | 5,294 |
| 5 | Practical Black-Box Attacks against Machine Learning Nicolas Papernot, Patrick Drew McDaniel, Ian Goodfellow et al. | 31 Mar 2017 | 3,549 |
| 6 | Domain randomization for transferring deep neural networks from simulation to the real world Josh Tobin, Rachel Fong, Alex Ray et al. | Sep 2017 | 2,932 |
| 7 | Diffusion Models Beat GANs on Image Synthesis Prafulla Dhariwal, Alex Nichol · arXiv (Cornell University) | 11 May 2021 | 2,191 |
| 8 | Learning dexterous in-hand manipulation OpenAI Marcin Andrychowicz, Bowen Baker, Maciek Chociej et al. · The International Journal of Robotics Research | 18 Nov 2019 | 1,642 |
| 9 | Diffusion Models: A Comprehensive Survey of Methods and Applications Ling Qiang Yang, Zhilong Zhang, Yang Song et al. · ACM Computing Surveys | 30 Sep 2023 | 1,565 |
| 10 | Scaling Laws for Neural Language Models Kaplan, Jared, Sam McCandlish, Tom Henighan et al. · arXiv (Cornell University) | 23 Jan 2020 | 1,542 |
| 11 | An evidence review of face masks against COVID-19 Jeremy Howard, Austin Huang, Zhiyuan Li et al. · Proceedings of the National Academy of Sciences | 11 Jan 2021 | 1,306 |
| 12 | InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen, Yan Duan, Rein Houthooft et al. · arXiv (Cornell University) | 12 Jun 2016 | 1,241 |
| 13 | Zero-Shot Text-to-Image Generation Aditya Ramesh, Mikhail Pavlov, Gabriel Goh et al. · arXiv (Cornell University) | 24 Feb 2021 | 1,141 |
| 14 | Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks Tim Salimans, Diederik P. Kingma · arXiv (Cornell University) | 25 Feb 2016 | 927 |
| 15 | Sim-to-Real Transfer of Robotic Control with Dynamics Randomization Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba et al. | May 2018 | 869 |
| 16 | Inferring single-trial neural population dynamics using sequential auto-encoders Chethan Pandarinath, Daniel J. O’Shea, Jasmine Collins et al. · Nature Methods | 14 Sep 2018 | 751 |
| 17 | GPTs are GPTs: Labor market impact potential of LLMs Tyna Eloundou, Sam Manning, Pamela Mishkin et al. · Science | 20 Jun 2024 | 737 |
| 18 | PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation Jason Ansel et al. | 22 Apr 2024 | 679 |
| 19 | Generative Pretraining From Pixels Mark I-Cheng Chen, Alec Radford, Rewon Child et al. · International Conference on Machine Learning | 12 Jul 2020 | 568 |
| 20 | Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Siobhan M. O'Connor, ChatGPT ChatGPT · Nurse Education in Practice | 16 Dec 2022 | 558 |
| 21 | Exploration in deep reinforcement learning: A survey Paweł Ładosz, Lilian Weng, Minwoo Kim et al. · Information Fusion | 31 Mar 2022 | 520 |
| 22 | Emergence of Grounded Compositional Language in Multi-Agent Populations Igor Mordatch, Pieter Abbeel · Proceedings of the AAAI Conference on Artificial Intelligence | 25 Apr 2018 | 440 |
| 23 | Reinforcement Learning with Deep Energy-Based Policies Tuomas Haarnoja, Haoran Tang, Pieter Abbeel et al. · arXiv (Cornell University) | 27 Feb 2017 | 433 |
| 24 | Improved Denoising Diffusion Probabilistic Models Alex Nichol, Prafulla Dhariwal · arXiv (Cornell University) | 18 Feb 2021 | 412 |
| 25 | Robust fine-tuning of zero-shot models Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim et al. · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | Jun 2022 | 397 |
| 26 | Generative Adversarial Imitation Learning Jonathan B. Ho, Stefano Ermon · arXiv (Cornell University) | 10 Jun 2016 | 367 |
| 27 | Large-Scale Study of Curiosity-Driven Learning Yuri Burda, Harri Edwards, Deepak Pathak et al. · arXiv (Cornell University) | 13 Aug 2018 | 366 |
| 28 | #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning Haoran Tang, Rein Houthooft, Davis Foote et al. · arXiv (Cornell University) | 15 Nov 2016 | 337 |
| 29 | Emergent Tool Use From Multi-Agent Autocurricula Bowen Baker, Ingmar Kanitscheider, Todor Markov et al. · arXiv (Cornell University) | 17 Sep 2019 | 334 |
| 30 | All the News That’s Fit to Fabricate: AI-Generated Text as a Tool of Media Misinformation Sarah E Kreps, R. Miles McCain, Miles Brundage · Journal of Experimental Political Science | 20 Nov 2020 | 315 |
| 31 | Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries The Global Dynamic Interventions Strategies for COVID-19 Collaborative Group, Rajiv Chowdhury, Kevin Heng et al. · European Journal of Epidemiology | May 2020 | 306 |
| 32 | Zoom In: An Introduction to Circuits Chris Olah, Nick Cammarata, Ludwig Schubert et al. · Distill | 10 Mar 2020 | 280 |
| 33 | Extracting Training Data from Large Language Models Nicholas Carlini, Florian Tramèr, Eric W. Wallace et al. · arXiv (Cornell University) | 14 Dec 2020 | 274 |
| 34 | Face Masks Against COVID-19: An Evidence Review Jeremy Howard, Austin Huang, Zhiyuan Li et al. · Preprints.org | 12 Apr 2020 | 273 |
| 35 | Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks Antreas Antoniou, Amos Storkey, Harrison S. Edwards · Lecture notes in computer science | 2018 | 270 |
| 36 | Exploration by Random Network Distillation Yuri Burda, Harrison S. Edwards, Amos Storkey et al. · arXiv (Cornell University) | 30 Oct 2018 | 254 |
| 37 | Cooperative AI: machines must learn to find common ground Allan Dafoe, Yoram Bachrach, Gillian K. Hadfield et al. · Nature | 4 May 2021 | 235 |
| 38 | Multimodal Neurons in Artificial Neural Networks Gabriel Goh, Nick Cammarata, Chelsea Voss et al. · Distill | 4 Mar 2021 | 232 |
| 39 | Value Iteration Networks Aviv Tamar, Yi Wu, Garrett Thomas et al. | 28 Jul 2017 | 228 |
| 40 | Rapamycin in the context of Pascal’s Wager: generative pre-trained transformer perspective ChatGPT Generative Pre-trained Transformer, Alex A. Zhavoronkov · Oncoscience | 21 Dec 2022 | 213 |
| 41 | First return, then explore Adrien Ecoffet, Joost Huizinga, Joel Lehman et al. · Nature | 24 Feb 2021 | 210 |
| 42 | Adversarial Training Methods for Semi-Supervised Text Classification Takeru Miyato, Andrew M. Dai, Ian Goodfellow · arXiv (Cornell University) | 25 May 2016 | 201 |
| 43 | Quantifying Generalization in Reinforcement Learning Karl Cobbe, Oleg Klimov, Chris Hesse et al. · arXiv (Cornell University) | 6 Dec 2018 | 195 |
| 44 | The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities Joel Lehman et al. · Artificial Life | 9 Apr 2020 | 185 |
| 45 | Leveraging Procedural Generation to Benchmark Reinforcement Learning Karl Cobbe, Christopher Hesse, Jacob K. Hilton et al. · arXiv (Cornell University) | 3 Dec 2019 | 169 |
| 46 | Collaboration between clinicians and vision–language models in radiology report generation Ryutaro Tanno et al. · Nature Medicine | 7 Nov 2024 | 168 |
| 47 | Sim2Real in Robotics and Automation: Applications and Challenges Sebastian Höfer, Kostas E. Bekris, Ankur Handa et al. · IEEE Transactions on Automation Science and Engineering | Apr 2021 | 165 |
| 48 | Universal photonic artificial intelligence acceleration Sufi R. Ahmed et al. · Nature | 9 Apr 2025 | 153 |
| 49 | Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations Josh A. Goldstein, Girish Sastry, Micah Musser et al. · arXiv (Cornell University) | 10 Jan 2023 | 148 |
| 50 | Deep double descent: where bigger models and more data hurt* Preetum Nakkiran, Gal Kaplun, Yamini Bansal et al. · Journal of Statistical Mechanics Theory and Experiment | Dec 2021 | 146 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for an institution when any of its authors gave it as an affiliation; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.