Institution · Meta (United States)
Most cited papers from Meta (United States)
Meta (United States)
What are the most cited papers from Meta (United States)?
The most cited paper from Meta (United States) is "Deep learning" (Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton, 2015), with 84,939 citations. Next come "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (55,999) and "Densely Connected Convolutional Networks" (45,976).
Showing 50 of 56 papers. Every paper with more than 1,338 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Deep learning Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton · Nature | 26 May 2015 | 84,939 |
| 2 | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 6 Jun 2016 | 55,999 |
| 3 | Densely Connected Convolutional Networks Gao Ming Huang, Zhuang Liu, Laurens van der Maaten et al. | Jul 2017 | 45,976 |
| 4 | Feature Pyramid Networks for Object Detection Tsung-Yi Lin, Piotr Dollár, Ross Girshick et al. | Jul 2017 | 29,985 |
| 5 | Mask R-CNN Kaiming He, Georgia Gkioxari, Piotr Dollár et al. | Oct 2017 | 29,752 |
| 6 | Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick et al. | Oct 2017 | 27,432 |
| 7 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Kumar Das et al. | Oct 2017 | 23,064 |
| 8 | Momentum Contrast for Unsupervised Visual Representation Learning Kaiming He, Haoqi Fan, Yuxin Wu et al. | Jun 2020 | 12,628 |
| 9 | Aggregated Residual Transformations for Deep Neural Networks Saining Xie, Ross Girshick, Piotr Dollár et al. | Jul 2017 | 12,067 |
| 10 | Non-local Neural Networks Xiaolong Wang, Ross Girshick, Abhinav Gupta et al. | Jun 2018 | 11,465 |
| 11 | Enriching Word Vectors with Subword Information Piotr Bojanowski, Édouard Grave, Armand Joulin et al. · Transactions of the Association for Computational Linguistics | Dec 2017 | 9,975 |
| 12 | Learning Spatiotemporal Features with 3D Convolutional Networks Du Tran, Lubomir Bourdev, Rob Fergus et al. | Dec 2015 | 9,921 |
| 13 | A ConvNet for the 2020s Zhuang Liu, Hanzi Mao, Chao-Yuan Wu et al. · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | Jun 2022 | 8,057 |
| 14 | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala · arXiv (Cornell University) | 19 Nov 2015 | 6,972 |
| 15 | DeepFace: Closing the Gap to Human-Level Performance in Face Verification Yaniv Taigman, Ming Juan Yang, Marc’Aurelio Ranzato et al. | Jun 2014 | 6,691 |
| 16 | Intriguing properties of neural networks Christian Szegedy, Wojciech Zaremba, Ilya Sutskever et al. · arXiv (Cornell University) | 21 Dec 2013 | 5,734 |
| 17 | Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron, Hugo Touvron, Ishan Misra et al. · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) | Oct 2021 | 5,566 |
| 18 | OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields Zhe Cao, Gines Hidalgo, Tomas Simon et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 17 Jul 2019 | 5,255 |
| 19 | Wasserstein Generative Adversarial Networks Martín Arjovsky, Soumith Chintala, Léon Bottou · International Conference on Machine Learning | 17 Jul 2017 | 5,047 |
| 20 | VQA: Visual Question Answering Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu et al. | Dec 2015 | 4,434 |
| 21 | SlowFast Networks for Video Recognition Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik et al. | Oct 2019 | 3,782 |
| 22 | Experimental evidence of massive-scale emotional contagion through social networks Adam D. I. Kramer, Jamie E. Guillory, Jeffrey T. Hancock · Proceedings of the National Academy of Sciences | 2 Jun 2014 | 3,752 |
| 23 | Geometric Deep Learning: Going beyond Euclidean data Michael M. Bronstein, Joan Bruna, Yann LeCun et al. · IEEE Signal Processing Magazine | Jul 2017 | 3,748 |
| 24 | Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers Sixiao Zheng, Jiachen Lu, Hengshuang Zhao et al. | Jun 2021 | 3,679 |
| 25 | A Closer Look at Spatiotemporal Convolutions for Action Recognition Du Tran, Heng Wang, Lorenzo Torresani et al. | Jun 2018 | 3,637 |
| 26 | Mask R-CNN Kaiming He, Georgia Gkioxari, Piotr Dollár et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 5 Jun 2018 | 3,506 |
| 27 | Exploring Simple Siamese Representation Learning Xinlei Chen, Kaiming He | Jun 2021 | 3,472 |
| 28 | Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences Alexander W. Rives, Joshua Meier, Tom Sercu et al. · Proceedings of the National Academy of Sciences | 5 Apr 2021 | 3,394 |
| 29 | Exposure to ideologically diverse news and opinion on Facebook Eytan Bakshy, Solomon Messing, Lada A. Adamic · Science | 7 May 2015 | 3,297 |
| 30 | You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon, Santosh Divvala, Ross Girshick et al. | Jun 2016 | 3,266 |
| 31 | Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Meta LLM-Integrated Systems Patrick Lewis, Ethan Perez, Aleksandara Piktus et al. · arXiv (Cornell University) | 22 May 2020 | 3,081 |
| 32 | Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture David Eigen, Rob Fergus | Dec 2015 | 2,927 |
| 33 | Cassandra Avinash Lakshman, Prashant Malik · ACM SIGOPS Operating Systems Review | 14 Apr 2010 | 2,672 |
| 34 | A 61-million-person experiment in social influence and political mobilization Robert M. Bond, Christopher J. Fariss, Jason Jeffrey Jones et al. · Nature | Sep 2012 | 2,639 |
| 35 | fairseq: A Fast, Extensible Toolkit for Sequence Modeling Myle Ott, Sergey Edunov, Alexei Baevski et al. | 2019 | 2,512 |
| 36 | Forecasting at Scale Sean J. Taylor, Benjamin Letham · The American Statistician | 29 Sep 2017 | 2,473 |
| 37 | In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication Baevski, Alexei, Zhou, Henry, Mohamed, Abdelrahman et al. · arXiv (Cornell University) | 20 Jun 2020 | 2,452 |
| 38 | Unsupervised Learning of Visual Features by Contrasting Cluster Assignments Mathilde Caron, Ishan Misra, Julien Mairal et al. · arXiv (Cornell University) | 17 Jun 2020 | 1,903 |
| 39 | Reading Tea Leaves: How Humans Interpret Topic Models Jonathan Chang, Sean Gerrish, Chong Wang et al. | 7 Dec 2009 | 1,879 |
| 40 | Apache Hadoop YARN Vinod Kumar Vavilapalli, Arun C. Murthy, Chris Douglas et al. | Oct 2013 | 1,865 |
| 41 | CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning Justin M. Johnson, Bharath Hariharan, Laurens van der Maaten et al. | Jul 2017 | 1,849 |
| 42 | Language Models as Knowledge Bases? Fabio Petroni, Tim Rocktäschel, Sebastian Riedel et al. | 2019 | 1,808 |
| 43 | Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Emily Denton, Soumith Chintala, Arthur D. Szlam et al. · arXiv (Cornell University) | 18 Jun 2015 | 1,659 |
| 44 | Hive Ashish Thusoo, Joydeep Sen Sarma, Namit Jain et al. · Proceedings of the VLDB Endowment | Aug 2009 | 1,553 |
| 45 | An Empirical Study of Training Self-Supervised Vision Transformers Xinlei Chen, Saining Xie, Kaiming He · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) | Oct 2021 | 1,548 |
| 46 | The role of social networks in information diffusion Eytan Bakshy, Itamar Rosenn, Cameron Marlow et al. | 16 Apr 2012 | 1,471 |
| 47 | DensePose: Dense Human Pose Estimation in the Wild. Güler, RA, Neverova, N, Kokkinos, I · UCL Discovery (University College London) | 17 Dec 2018 | 1,469 |
| 48 | Reading Wikipedia to Answer Open-Domain Questions Danqi Chen, Adam J Fisch, Jason Weston et al. | 2017 | 1,463 |
| 49 | Is Space-Time Attention All You Need for Video Understanding? Gedas Bertasius, Heng Wang, Lorenzo Torresani · arXiv (Cornell University) | 9 Feb 2021 | 1,460 |
| 50 | Panoptic Feature Pyramid Networks Alexander Kirillov, Ross Girshick, Kaiming He et al. | Jun 2019 | 1,419 |
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.