Subfield · Artificial Intelligence
Most cited papers in Artificial Intelligence
What are the most cited papers in Artificial Intelligence?
The most cited paper in Artificial Intelligence is "Deep Residual Learning for Image Recognition" (Kaiming He, Xiangyu Zhang, Shaoqing Ren et al., 2016), with 229,585 citations. Next come "Random Forests" (132,956) and "Long Short-Term Memory" (102,418).
Showing 50 of 98 papers. Every paper with more than 16,492 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. | Jun 2016 | 229,585 |
| 2 | Random Forests Leo Breiman · Machine Learning | Oct 2001 | 132,956 |
| 3 | Long Short-Term Memory Sepp Hochreiter, Jürgen Schmidhuber · Neural Computation | Nov 1997 | 102,418 |
| 4 | Fitting Linear Mixed-Effects Models Using lme4 Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software | 2015 | 89,183 |
| 5 | Deep learning Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton · Nature | 26 May 2015 | 84,939 |
| 6 | Adam: A Method for Stochastic Optimization Diederik P. Kingma, Jimmy Lei Ba · arXiv (Cornell University) | 22 Dec 2014 | 84,625 |
| 7 | Exploiting Generative AI to Scale up Intelligent Tutoring Systems Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) | 2023 | 79,061 |
| 8 | ImageNet classification with deep convolutional neural networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · Communications of the ACM | 24 May 2017 | 75,634 |
| 9 | Gapped BLAST and PSI-BLAST: a new generation of protein database search programs Stephen F. Altschul · Nucleic Acids Research | Sep 1997 | 75,032 |
| 10 | The Sequence Alignment/Map format and SAMtools Heng Li, Bob Handsaker, Alec Wysoker et al. · Bioinformatics | 8 Jun 2009 | 69,061 |
| 11 | Scikit-learn: Machine Learning in Python Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · ORBi (University of Liège) | 2 Jan 2012 | 64,016 |
| 12 | Fast and accurate short read alignment with Burrows–Wheeler transform Heng Li, Richard M. Durbin · Bioinformatics | 18 May 2009 | 63,989 |
| 13 | Fast gapped-read alignment with Bowtie 2 Ben Langmead, Steven L. Salzberg · Nature Methods | 4 Mar 2012 | 62,825 |
| 14 | Gradient-based learning applied to document recognition Yann LeCun, Léon Bottou, Yoshua Bengio et al. · Proceedings of the IEEE | 1998 | 59,921 |
| 15 | XGBoost Tianqi Chen, Carlos Guestrin | 8 Aug 2016 | 53,418 |
| 16 | Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support Paul A. Harris, ROBERT J. TAYLOR, Robert J. Thielke et al. · Journal of Biomedical Informatics | Oct 2008 | 53,362 |
| 17 | Regression Shrinkage and Selection Via the Lasso Robert John Tibshirani · Journal of the Royal Statistical Society Series B (Statistical Methodology) | 1996 | 53,131 |
| 18 | A new look at the statistical model identification Hirotugu Akaike · IEEE Transactions on Automatic Control | Dec 1974 | 51,685 |
| 19 | Adam: A Method for Stochastic Optimization Mandi, Jayanta, Canoy, Rocsildes, Bucarey, Víctor et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) | May 2015 | 50,327 |
| 20 | Maximum Likelihood from Incomplete Data Via the EM Algorithm A. P. Dempster, N. M. Laird, Donald B. Rubin · Journal of the Royal Statistical Society Series B (Statistical Methodology) | Sep 1977 | 50,034 |
| 21 | A fast and elitist multiobjective genetic algorithm: NSGA-II Kalyanmoy Deb, Amrit Pratap, Sakshi Agarwal et al. · IEEE Transactions on Evolutionary Computation | Apr 2002 | 49,664 |
| 22 | Particle swarm optimization James C. Kennedy, R.C. Eberhart | 19 Nov 2002 | 48,693 |
| 23 | Going deeper with convolutions Christian Szegedy, Wei Liu, Yangqing Jia et al. | Jun 2015 | 47,422 |
| 24 | AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale Kefallinos, Dionysios, Alexandris, Georgios, Maras, Alexis et al. · arXiv (Cornell University) | 11 Oct 2018 | 46,030 |
| 25 | Densely Connected Convolutional Networks Gao Ming Huang, Zhuang Liu, Laurens van der Maaten et al. | Jul 2017 | 45,976 |
| 26 | Fast Parallel Algorithms for Short-Range Molecular Dynamics Steven J. Plimpton · Journal of Computational Physics | Mar 1995 | 45,350 |
| 27 | CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP Joseph Felsenstein · Evolution | Jul 1985 | 41,763 |
| 28 | LIBSVM Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology | Apr 2011 | 41,500 |
| 29 | Support-vector networks Corinna Cortes, Vladimir N. Vapnik · Machine Learning | Sep 1995 | 41,211 |
| 30 | Matplotlib: A 2D Graphics Environment John D. Hunter · Computing in Science & Engineering | 2007 | 41,156 |
| 31 | SciPy 1.0: fundamental algorithms for scientific computing in Python Pauli Virtanen et al. · Nature Methods | 3 Feb 2020 | 40,161 |
| 32 | MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms Sudhir Kumar, Glen Stecher, Michael M. Li et al. · Molecular Biology and Evolution | May 2018 | 39,563 |
| 33 | The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools Julie Thompson · Nucleic Acids Research | 15 Dec 1997 | 39,325 |
| 34 | Fully convolutional networks for semantic segmentation Jonathan Long, Evan Shelhamer, Trevor J. Darrell | Jun 2015 | 37,703 |
| 35 | Dropout: a simple way to prevent neural networks from overfitting Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al. | 2014 | 34,210 |
| 36 | Glove: Global Vectors for Word Representation Jeffrey Pennington, Richard Socher, Christopher D. Manning | 2014 | 34,197 |
| 37 | Prospect theory: An analysis of decision under risk Daniel J. Kahneman, Amos Tversky · Cambridge University Press eBooks | 25 Mar 1988 | 32,957 |
| 38 | Swin Transformer: Hierarchical Vision Transformer using Shifted Windows Ze Liu, Yutong Lin, Yue Cao et al. · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) | Oct 2021 | 32,838 |
| 39 | SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall et al. · Journal of Artificial Intelligence Research | Jun 2002 | 32,759 |
| 40 | Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation Ross Girshick, Jeff Donahue, Trevor J. Darrell et al. | Jun 2014 | 32,284 |
| 41 | Learning representations by back-propagating errors David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams · Nature | Oct 1986 | 32,003 |
| 42 | fastp: an ultra-fast all-in-one FASTQ preprocessor Shifu Chen, Yanqing Zhou, Yaru Chen et al. · Bioinformatics | 7 Jul 2018 | 31,650 |
| 43 | Rethinking the Inception Architecture for Computer Vision Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe et al. | Jun 2016 | 31,624 |
| 44 | The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data Aaron McKenna, Matthew G. Hanna, Eric Banks et al. · Genome Research | 19 Jul 2010 | 30,696 |
| 45 | Greedy function approximation: A gradient boosting machine. Jerome H. Friedman · The Annals of Statistics | Oct 2001 | 30,593 |
| 46 | Squeeze-and-Excitation Networks Jie Hu, Li Shen, Gang Zheng Sun | Jun 2018 | 30,332 |
| 47 | Feature Pyramid Networks for Object Detection Tsung-Yi Lin, Piotr Dollár, Ross Girshick et al. | Jul 2017 | 29,985 |
| 48 | IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies Lam-Tung Nguyen, Heiko A. Schmidt, Arndt von Haeseler et al. · Molecular Biology and Evolution | 3 Nov 2014 | 28,418 |
| 49 | Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick et al. | Oct 2017 | 27,432 |
| 50 | Latent dirichlet allocation David M. Blei, Andrew Y. Ng, Michael I. Jordan · Journal of Machine Learning Research | Mar 2003 | 27,026 |
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 a field when OpenAlex files it under one of the field's topics; 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.