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Topic · Neural Networks and Applications

Most cited papers in Neural Networks and Applications

Neural Networks and Applications

What are the most cited papers in Neural Networks and Applications?

The most cited paper in Neural Networks and Applications is "Random Forests" (Leo Breiman, 2001), with 132,956 citations. Next come "Long Short-Term Memory" (102,418) and "Deep learning" (84,939).

Showing 50 of 97 papers. Every paper with more than 4,313 citations is listed.

Most cited papers, sortable by citations and release date
#Paper
1 Random Forests Leo Breiman · Machine Learning Open access Oct 2001 132,956
2 Long Short-Term Memory Sepp Hochreiter, Jürgen Schmidhuber · Neural Computation Nov 1997 102,418
3 Deep learning Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton · Nature Open access 26 May 2015 84,939
4 Gradient-based learning applied to document recognition Yann LeCun, Léon Bottou, Yoshua Bengio et al. · Proceedings of the IEEE Open access 1998 59,921
5 Particle swarm optimization James C. Kennedy, R.C. Eberhart Conference paper 19 Nov 2002 48,693
6 LIBSVM Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology Apr 2011 41,500
7 Support-vector networks Corinna Cortes, Vladimir N. Vapnik · Machine Learning Open access Sep 1995 41,211
8 Dropout: a simple way to prevent neural networks from overfitting Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al. 2014 34,210
9 Learning representations by back-propagating errors David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams · Nature Oct 1986 32,003
10 Greedy function approximation: A gradient boosting machine. Jerome H. Friedman · The Annals of Statistics Oct 2001 30,593
11 Statistical Learning Theory Yuhai Wu, Vladimir N. Vapnik · Technometrics Nov 1999 26,900
12 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe, Christian Szegedy · arXiv (Cornell University) PreprintOpen access 11 Feb 2015 24,392
13 Investigating Causal Relations by Econometric Models and Cross-spectral Methods Clive W. J. Granger · Econometrica Aug 1969 23,275
14 Multilayer feedforward networks are universal approximators Kurt Hornik, Maxwell B. Stinchcombe, Halbert White · Neural Networks 1989 21,792
15 Regularization and Variable Selection Via the Elastic Net Hui Jun Zou, Trevor Hastie · Journal of the Royal Statistical Society Series B (Statistical Methodology) Open access 9 Mar 2005 21,757
16 Reducing the Dimensionality of Data with Neural Networks Geoffrey E. Hinton, Ruslan Salakhutdinov · Science 27 Jul 2006 21,368
17 Fuzzy identification of systems and its applications to modeling and control Tomohiro Takagi, Michio Sugeno · IEEE Transactions on Systems Man and Cybernetics 1985 19,579
18 Deep learning in neural networks: An overview Jürgen Schmidhuber · Neural Networks Open access 13 Oct 2014 18,327
19 A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Whitney K. Newey, Kenneth D. West · Econometrica May 1987 17,610
20 Bagging Predictors Leo Breiman · Machine Learning Open access Aug 1996 17,320
21 A Fast Learning Algorithm for Deep Belief Nets Geoffrey E. Hinton, Simon Osindero, Yee‐Whye Teh · Neural Computation 29 May 2006 16,607
22 A Tutorial on Support Vector Machines for Pattern Recognition Christopher J. C. Burges · Data Mining and Knowledge Discovery Jun 1998 16,499
23 Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers Stephen Boyd, Neal Parikh, Eric Chu et al. · Foundations and Trends® in Machine Learning 26 Jul 2011 16,474
24 ANFIS: adaptive-network-based fuzzy inference system Jyh‐Shing Roger Jang · IEEE Transactions on Systems Man and Cybernetics 1993 16,328
25 A new optimizer using particle swarm theory R.C. Eberhart, James C. Kennedy Conference paper 19 Nov 2002 15,000
26 Learning the parts of objects by non-negative matrix factorization Daniel D. Lee, H. Sebastian Seung · Nature Oct 1999 14,302
27 Approximation by superpositions of a sigmoidal function George V. Cybenko · Mathematics of Control Signals and Systems Open access Dec 1989 14,001
28 A tutorial on support vector regression Alex J. Smola, Bernhard Schölkopf · Statistics and Computing 19 Jul 2004 13,458
29 Extreme learning machine: Theory and applications Guang-Bin Huang, Qinyu Zhu, Chee‐Kheong Siew · Neurocomputing 17 May 2006 13,359
30 Representation Learning: A Review and New Perspectives Yoshua Bengio, Aaron C. Courville, P. M. Durai Raj Vincent · IEEE Transactions on Pattern Analysis and Machine Intelligence 31 May 2013 13,351
31 The Elements of Statistical Learning: Data Mining, Inference, and Prediction Zigang Lu · Journal of the Royal Statistical Society Series A (Statistics in Society) 17 Jun 2010 12,808
32 Squeeze-and-Excitation Networks Jie Hu, Li Shen, Samuel Albanie et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence 2 May 2019 12,749
33 Understanding the difficulty of training deep feedforward neural networks Xavier Glorot, Yoshua Bengio 2010 12,654
34 Backpropagation Applied to Handwritten Zip Code Recognition Yann LeCun, Bernhard E. Boser, J. S. Denker et al. · Neural Computation Dec 1989 12,102
35 A training algorithm for optimal margin classifiers Bernhard E. Boser, Isabelle Guyon, Vladimir N. Vapnik Conference paper Jul 1992 11,724
36 Fundamentals of Statistical Signal Processing: Estimation Theory Sailes K. Sengijpta · Technometrics Nov 1995 11,254
37 SExtractor: Software for source extraction E. Bertin, Stéphane Arnouts · Astronomy & Astrophysics Supplement Series Open access Jun 1996 11,111
38 Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling Jun‐Young Chung, Çağlar Gülçehre, Kyunghyun Cho et al. · arXiv (Cornell University) PreprintOpen access 11 Dec 2014 10,801
39 Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy Hanchuan Peng, Fuhui Long, Chen Ding · IEEE Transactions on Pattern Analysis and Machine Intelligence 22 Jun 2005 10,690
40 Bidirectional recurrent neural networks Mike Schuster, Kuldip K. Paliwal · IEEE Transactions on Signal Processing 1997 10,411
41 The Graph Neural Network Model Franco Scarselli, M. Gori, Ah Chung Tsoi et al. · IEEE Transactions on Neural Networks Open access 10 Dec 2008 9,764
42 Least Squares Support Vector Machine Classifiers Johan A. K. Suykens, Joos P. L. Vandewalle · Neural Processing Letters Open access Jun 1999 9,454
43 An Information-Maximization Approach to Blind Separation and Blind Deconvolution Anthony J. Bell, Terrence J. Sejnowski · Neural Computation Nov 1995 9,265
44 Extremely randomized trees Pierre Geurts, Damien Ernst, Louis A. Wehenkel · Machine Learning Open access 2 Mar 2006 9,239
45 Independent component analysis: algorithms and applications Aapo Hyvärinen, Erkki Oja · Neural Networks Jun 2000 8,914
46 Reinforcement Learning: An Introduction Jeffrey D. Johnson, Jinghong Li, Zengshi Chen · Neurocomputing Nov 2000 8,698
47 Learning long-term dependencies with gradient descent is difficult Yoshua Bengio, P. Simard, Paolo Frasconi · IEEE Transactions on Neural Networks Mar 1994 8,685
48 Independent component analysis, A new concept? Pierre Comon · Signal Processing Open access Apr 1994 8,300
49 The self-organizing map Teuvo Kohonen · Proceedings of the IEEE 1990 8,261
50 Nonlinear Component Analysis as a Kernel Eigenvalue Problem Bernhard Schölkopf, Alexander Johannes Smola, Klaus‐Robert Müller · Neural Computation Jul 1998 8,164

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.