Institution · Nvidia (United States)
Most cited papers from Nvidia (United States)
What are the most cited papers from Nvidia (United States)?
The most cited paper from Nvidia (United States) is "High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs" (Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu et al., 2018), with 4,526 citations. Next come "Efficient Processing of Deep Neural Networks: A Tutorial and Survey" (4,237) and "Instant neural graphics primitives with a multiresolution hash encoding" (4,064).
Showing 50 of 60 papers. Every paper with more than 576 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu et al. | Jun 2018 | 4,526 |
| 2 | Efficient Processing of Deep Neural Networks: A Tutorial and Survey Vivienne Sze, Yu‐Hsin Chen, Tien-Ju Yang et al. · Proceedings of the IEEE | 20 Nov 2017 | 4,237 |
| 3 | Instant neural graphics primitives with a multiresolution hash encoding Thomas J. Muller, Alex Evans, Christoph Schied et al. · ACM Transactions on Graphics | Jul 2022 | 4,064 |
| 4 | Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald Romelia Salomón–Ferrer, Andreas W. Götz, Duncan Poole et al. · Journal of Chemical Theory and Computation | 6 Aug 2013 | 3,629 |
| 5 | Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han, Huizi Mao, William J. Dally · arXiv (Cornell University) | Oct 2015 | 3,528 |
| 6 | Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks Yu-Hsin Chen, Tushar Krishna, Joel Emer et al. · IEEE Journal of Solid-State Circuits | 8 Nov 2016 | 3,230 |
| 7 | The future of digital health with federated learning Nicola Rieke, Jonny Hancox, Wenqi Li et al. · npj Digital Medicine | 14 Sep 2020 | 3,041 |
| 8 | Loss Functions for Image Restoration With Neural Networks Hang Zhao, Orazio Gallo, Iuri Frosio et al. · IEEE Transactions on Computational Imaging | 24 Dec 2016 | 2,716 |
| 9 | Multimodal Unsupervised Image-to-Image Translation Xun Cheng Huang, Ming-Yu Liu, Serge Belongie et al. · Lecture notes in computer science | 2018 | 2,591 |
| 10 | ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning Ahmed Elnaggar, Michael Heinzinger, Christian Dallago et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 7 Jul 2021 | 2,489 |
| 11 | Image Inpainting for Irregular Holes Using Partial Convolutions Guilin Liu, Fitsum A. Reda, Kevin J. Shih et al. · Lecture notes in computer science | 2018 | 2,442 |
| 12 | Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born Andreas W. Götz, Mark J. Williamson, Dong Xu et al. · Journal of Chemical Theory and Computation | 26 Mar 2012 | 2,170 |
| 13 | PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes Xiang Yu, Tanner Schmidt, Venkatraman Narayanan et al. | 26 Jun 2018 | 2,147 |
| 14 | Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations Maziar Raissi, Alireza Z. K. Yazdani, George Em Karniadakis · Science | 30 Jan 2020 | 2,145 |
| 15 | EIE Song Han, Xingyu Liu, Huizi Mao et al. · ACM SIGARCH Computer Architecture News | 18 Jun 2016 | 2,047 |
| 16 | A Survey of General‐Purpose Computation on Graphics Hardware John D. Owens, David P. Luebke, Naga K. Govindaraju et al. · Computer Graphics Forum | Mar 2007 | 2,034 |
| 17 | Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang et al. · Lecture notes in computer science | 2022 | 1,898 |
| 18 | Scientific discovery in the age of artificial intelligence Hanchen Wang et al. · Nature | 2 Aug 2023 | 1,788 |
| 19 | Scalable parallel programming with CUDA John R. Nickolls, Ian Buck, Michael J. Garland et al. | 11 Aug 2008 | 1,618 |
| 20 | Progressive Growing of GANs for Improved Quality, Stability, and Variation Tero Karras, Timo Aila, Samuli Laine et al. · arXiv (Cornell University) | 27 Oct 2017 | 1,552 |
| 21 | Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar et al. · Lecture notes in computer science | 2018 | 1,500 |
| 22 | GPU Computing John D. Owens, Michael J. Houston, David P. Luebke et al. · Proceedings of the IEEE | 16 Apr 2008 | 1,483 |
| 23 | The Medical Segmentation Decathlon Michela Antonelli et al. · Nature Communications | 15 Jul 2022 | 1,309 |
| 24 | PRIME Ping Chi, Shuangchen Li, Cong Xu et al. · ACM SIGARCH Computer Architecture News | 18 Jun 2016 | 1,252 |
| 25 | The Liver Tumor Segmentation Benchmark (LiTS) Patrick Bilic et al. · Medical Image Analysis | 17 Nov 2022 | 1,236 |
| 26 | Eyeriss Yu‐Hsin Chen, Joel Emer, Vivienne Sze · ACM SIGARCH Computer Architecture News | 18 Jun 2016 | 1,230 |
| 27 | Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices Yu-Hsin Chen, Tien-Ju Yang, Joel Emer et al. · IEEE Journal on Emerging and Selected Topics in Circuits and Systems | Jun 2019 | 1,109 |
| 28 | Noise2Noise: Learning Image Restoration without Clean Data Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren et al. · arXiv (Cornell University) | 12 Mar 2018 | 991 |
| 29 | Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking Jinkun Cao, Jiangmiao Pang, Xinshuo Weng et al. | Jun 2023 | 988 |
| 30 | The GPU Computing Era John R. Nickolls, William J. Dally · IEEE Micro | Mar 2010 | 978 |
| 31 | Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS Szilárd Páll, Artem Zhmurov, Paul Bauer et al. · The Journal of Chemical Physics | 5 Oct 2020 | 962 |
| 32 | SCNN Angshuman Parashar, Minsoo Rhu, Anurag Mukkara et al. | 15 Jun 2017 | 939 |
| 33 | SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers Enze Xie, Wenhai Wang, Zhiding Yu et al. · CaltechAUTHORS (California Institute of Technology) | 31 May 2021 | 911 |
| 34 | Optimization principles and application performance evaluation of a multithreaded GPU using CUDA Shane Ryoo, Christopher I. Rodrigues, Sara S. Baghsorkhi et al. | 20 Feb 2008 | 910 |
| 35 | Implementing sparse matrix-vector multiplication on throughput-oriented processors Nathan Bell, Michael J. Garland | 14 Nov 2009 | 909 |
| 36 | A large language model for electronic health records Xi Yang, Aokun Chen, Nima PourNejatian et al. · npj Digital Medicine | 26 Dec 2022 | 884 |
| 37 | Joint Discriminative and Generative Learning for Person Re-Identification Zhedong Zheng, Xiaodong Yang, Zhiding Yu et al. | Jun 2019 | 874 |
| 38 | Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds Haroon Idrees, Muhmmad Tayyab, Kishan Athrey et al. · Lecture notes in computer science | 2018 | 848 |
| 39 | Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism Mohammad Shoeybi, Mostofa Ali Patwary, Raul Puri et al. · arXiv (Cornell University) | 17 Sep 2019 | 833 |
| 40 | Magic3D: High-Resolution Text-to-3D Content Creation Chen-Hsuan Lin, Jun Gao, Luming Tang et al. | Jun 2023 | 808 |
| 41 | Federated learning for predicting clinical outcomes in patients with COVID-19 Ittai Dayan et al. · Nature Medicine | 15 Sep 2021 | 806 |
| 42 | A detailed and flexible cycle-accurate Network-on-Chip simulator Nan Jiang, James D. Balfour, Daniel U. Becker et al. | Apr 2013 | 798 |
| 43 | TransMorph: Transformer for unsupervised medical image registration Junyu Chen, Eric C. Frey, Yufan He et al. · Medical Image Analysis | 14 Sep 2022 | 782 |
| 44 | A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras, Samuli Laine, Timo Aila · IEEE Transactions on Pattern Analysis and Machine Intelligence | Jun 2019 | 766 |
| 45 | Implementing molecular dynamics on hybrid high performance computers – short range forces William M. Brown, Peng Wang, Steven J. Plimpton et al. · Computer Physics Communications | 23 Dec 2010 | 714 |
| 46 | NWChem: Past, present, and future E. Aprà et al. · The Journal of Chemical Physics | 11 May 2020 | 710 |
| 47 | OptiX Steven G. Parker, James Bigler, Andreas J. Dietrich et al. · ACM Transactions on Graphics | 15 Jul 2010 | 688 |
| 48 | Physics-informed machine learning: case studies for weather and climate modelling Karthik Kashinath et al. · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences | 15 Feb 2021 | 683 |
| 49 | NWChem: Past, Present, and Future Aprà, E et al. · Civil War Book Review | 2020 | 671 |
| 50 | GPUs and the Future of Parallel Computing Stephen W. Keckler, William J. Dally, Brucek Khailany et al. · IEEE Micro | Sep 2011 | 668 |
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.