Field · Computer Science
Most cited papers in Computer Science
What are the most cited papers in Computer Science?
The most cited paper in Computer Science 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 99 papers. Every paper with more than 22,754 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 | U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger, Philipp F. Fischer, Thomas Brox · Lecture notes in computer science | 2015 | 92,751 |
| 5 | Fitting Linear Mixed-Effects Models Using lme4 Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software | 2015 | 89,183 |
| 6 | A short history of SHELX George M. Sheldrick · Acta Crystallographica Section A Foundations of Crystallography | 21 Dec 2007 | 87,903 |
| 7 | Deep learning Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton · Nature | 26 May 2015 | 84,939 |
| 8 | Adam: A Method for Stochastic Optimization Diederik P. Kingma, Jimmy Lei Ba · arXiv (Cornell University) | 22 Dec 2014 | 84,625 |
| 9 | 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 |
| 10 | ImageNet classification with deep convolutional neural networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · Communications of the ACM | 24 May 2017 | 75,634 |
| 11 | Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan, Andrew Zisserman · arXiv (Cornell University) | 4 Sep 2014 | 75,434 |
| 12 | Gapped BLAST and PSI-BLAST: a new generation of protein database search programs Stephen F. Altschul · Nucleic Acids Research | Sep 1997 | 75,032 |
| 13 | The Sequence Alignment/Map format and SAMtools Heng Li, Bob Handsaker, Alec Wysoker et al. · Bioinformatics | 8 Jun 2009 | 69,061 |
| 14 | Scikit-learn: Machine Learning in Python Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · ORBi (University of Liège) | 2 Jan 2012 | 64,016 |
| 15 | Fast and accurate short read alignment with Burrows–Wheeler transform Heng Li, Richard M. Durbin · Bioinformatics | 18 May 2009 | 63,989 |
| 16 | ImageNet: A large-scale hierarchical image database Jia Hao Deng, Wei Dong, Richard Socher et al. · 2009 IEEE Conference on Computer Vision and Pattern Recognition | Jun 2009 | 63,518 |
| 17 | Fast gapped-read alignment with Bowtie 2 Ben Langmead, Steven L. Salzberg · Nature Methods | 4 Mar 2012 | 62,825 |
| 18 | Evaluating Structural Equation Models with Unobservable Variables and Measurement Error Claes Fornell, David F. Larcker · Journal of Marketing Research | Feb 1981 | 61,338 |
| 19 | Gradient-based learning applied to document recognition Yann LeCun, Léon Bottou, Yoshua Bengio et al. · Proceedings of the IEEE | 1998 | 59,921 |
| 20 | Image quality assessment: from error visibility to structural similarity Zhou Wang, Alan Conrad Bovik, Hamid R. Sheikh et al. · IEEE Transactions on Image Processing | Apr 2004 | 58,409 |
| 21 | Distinctive Image Features from Scale-Invariant Keypoints David G. Lowe · International Journal of Computer Vision | 2 Jun 2004 | 56,041 |
| 22 | 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 |
| 23 | XGBoost Tianqi Chen, Carlos Guestrin | 8 Aug 2016 | 53,418 |
| 24 | 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 |
| 25 | Regression Shrinkage and Selection Via the Lasso Robert John Tibshirani · Journal of the Royal Statistical Society Series B (Statistical Methodology) | 1996 | 53,131 |
| 26 | A new look at the statistical model identification Hirotugu Akaike · IEEE Transactions on Automatic Control | Dec 1974 | 51,685 |
| 27 | 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 |
| 28 | 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 |
| 29 | 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 |
| 30 | Particle swarm optimization James C. Kennedy, R.C. Eberhart | 19 Nov 2002 | 48,693 |
| 31 | Going deeper with convolutions Christian Szegedy, Wei Liu, Yangqing Jia et al. | Jun 2015 | 47,422 |
| 32 | 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 |
| 33 | Densely Connected Convolutional Networks Gao Ming Huang, Zhuang Liu, Laurens van der Maaten et al. | Jul 2017 | 45,976 |
| 34 | Fast Parallel Algorithms for Short-Range Molecular Dynamics Steven J. Plimpton · Journal of Computational Physics | Mar 1995 | 45,350 |
| 35 | Optimization by Simulated Annealing Scott Kirkpatrick, C. D. Gelatt, Mario P. Vecchi · Science | 13 May 1983 | 45,251 |
| 36 | Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach Andrew F. Hayes | 6 May 2013 | 45,150 |
| 37 | Coefficient Alpha and the Internal Structure of Tests Lee J. Cronbach · Psychometrika | Sep 1951 | 44,025 |
| 38 | Microsoft COCO: Common Objects in Context Tsung-Yi Lin, Michael Maire, Serge Belongie et al. · Lecture notes in computer science | 2014 | 43,802 |
| 39 | Collective dynamics of ‘small-world’ networks Duncan J. Watts, Steven H. Strogatz · Nature | Jun 1998 | 43,707 |
| 40 | CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP Joseph Felsenstein · Evolution | Jul 1985 | 41,763 |
| 41 | LIBSVM Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology | Apr 2011 | 41,500 |
| 42 | Support-vector networks Corinna Cortes, Vladimir N. Vapnik · Machine Learning | Sep 1995 | 41,211 |
| 43 | Matplotlib: A 2D Graphics Environment John D. Hunter · Computing in Science & Engineering | 2007 | 41,156 |
| 44 | ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky, Jia Hao Deng, Hao Su et al. · International Journal of Computer Vision | 10 Apr 2015 | 40,843 |
| 45 | Structural equation modeling in practice: A review and recommended two-step approach. James C. Anderson, David W. Gerbing · Psychological Bulletin | May 1988 | 40,395 |
| 46 | SciPy 1.0: fundamental algorithms for scientific computing in Python Pauli Virtanen et al. · Nature Methods | 3 Feb 2020 | 40,161 |
| 47 | 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 |
| 48 | 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 |
| 49 | AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading Oleg Trott, Arthur J. Olson · Journal of Computational Chemistry | 4 Jun 2009 | 38,730 |
| 50 | Fully convolutional networks for semantic segmentation Jonathan Long, Evan Shelhamer, Trevor J. Darrell | Jun 2015 | 37,703 |
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.