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Most cited papers in Machine Learning and ELM

Machine Learning and ELM

What are the most cited papers in Machine Learning and ELM?

The most cited paper in Machine Learning and ELM is "Adam: A Method for Stochastic Optimization" (Diederik P. Kingma, Jimmy Lei Ba, 2014), with 84,625 citations. Next come "Adam: A Method for Stochastic Optimization" (50,327) and "Support-vector networks" (41,211).

Showing 50 of 100 papers. Every paper with more than 696 citations is listed.

Most cited papers, sortable by citations and release date
#Paper
1 Adam: A Method for Stochastic Optimization Diederik P. Kingma, Jimmy Lei Ba · arXiv (Cornell University) PreprintOpen access 22 Dec 2014 84,625
2 Adam: A Method for Stochastic Optimization Mandi, Jayanta, Canoy, Rocsildes, Bucarey, Víctor et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) Open access May 2015 50,327
3 Support-vector networks Corinna Cortes, Vladimir N. Vapnik · Machine Learning Open access Sep 1995 41,211
4 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
5 Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories Chen, Tianyu, Wang, Zeyu, Li, Lin et al. · Leibniz international proceedings in informatics Conference paperOpen access 2025 16,321
6 Extreme learning machine: Theory and applications Guang-Bin Huang, Qinyu Zhu, Chee‐Kheong Siew · Neurocomputing 17 May 2006 13,359
7 A Comprehensive Survey on Transfer Learning Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan et al. · Proceedings of the IEEE 7 Jul 2020 6,487
8 Approximation capabilities of multilayer feedforward networks Kurt Hornik · Neural Networks 1991 6,251
9 SCA: A Sine Cosine Algorithm for solving optimization problems Seyedali Mirjalili · Knowledge-Based Systems 6 Jan 2016 5,733
10 Extreme Learning Machine for Regression and Multiclass Classification Guang-Bin Huang, Hongming Zhou, Xiaojian Ding et al. · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 11 Oct 2011 5,594
11 Fast Training of Support Vector Machines Using Sequential Minimal Optimization John C. Platt · The MIT Press eBooks Book chapter Dec 1998 5,489
12 Deep Sparse Rectifier Neural Networks Xavier Glorot, Antoine Bordes, Yoshua Bengio 11 Apr 2011 5,420
13 Extreme learning machine: a new learning scheme of feedforward neural networks Guang-Bin Huang, Qin‐Yu Zhu, Chee‐Kheong Siew Conference paper 5 Apr 2005 4,121
14 A scaled conjugate gradient algorithm for fast supervised learning Martin F. Møller · Neural Networks 1993 4,094
15 A direct adaptive method for faster backpropagation learning: the RPROP algorithm Martin Riedmiller, Heinrich Braun Conference paper 30 Dec 2002 3,938
16 Gradient boosting machines, a tutorial Alexey Natekin, Alois Knoll · Frontiers in Neurorobotics Open access 2013 3,933
17 A survey of decision tree classifier methodology S.R. Safavian, D. A. Landgrebe · IEEE Transactions on Systems Man and Cybernetics Open access 1991 3,860
18 State-of-the-art in artificial neural network applications: A survey Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara et al. · Heliyon Open access Nov 2018 3,086
19 A Survey on Deep Transfer Learning Chuanqi Tan, Fuchun Sun, Tao Kong et al. · Lecture notes in computer science Conference paper 2018 2,920
20 Generalizing from a Few Examples Yaqing Wang, Quanming Yao, James Tin-Yau Kwok et al. · ACM Computing Surveys 12 Jun 2020 2,782
21 Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes Guang-Bin Huang, Lei Chen, Chee‐Kheong Siew · IEEE Transactions on Neural Networks Jul 2006 2,637
22 Random Features for Large-Scale Kernel Machines Ali Rahimi, Benjamin Recht 3 Dec 2007 2,633
23 On the momentum term in gradient descent learning algorithms Ning Qian · Neural Networks Open access 1999 2,376
24 A Survey on Multi-Task Learning Yu Zhang, Qiang Yang · IEEE Transactions on Knowledge and Data Engineering 31 Mar 2021 2,350
25 Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory Sumio Watanabe · arXiv (Cornell University) PreprintOpen access 14 Apr 2010 2,345
26 Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter · arXiv (Cornell University) PreprintOpen access 23 Nov 2015 2,305
27 Ensemble deep learning: A review M. A. Ganaie, Minghui Hu, A. K. Malik et al. · Engineering Applications of Artificial Intelligence Open access 30 Jul 2022 2,154
28 A Cooperative Approach to Particle Swarm Optimization F. vandenBergh, Andries Petrus Engelbrecht · IEEE Transactions on Evolutionary Computation Jun 2004 2,142
29 Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks Kurt Hornik, Maxwell B. Stinchcombe, Halbert White · Neural Networks 1990 2,138
30 Deep Interest Network for Click-Through Rate Prediction Guorui Zhou, Xiaoqiang Zhu, Chenru Song et al. Conference paper 19 Jul 2018 2,092
31 Transfer Feature Learning with Joint Distribution Adaptation Mingsheng Long, Jianmin Wang, Guiguang Ding et al. Conference paper Dec 2013 2,036
32 A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks Nanying Liang, Guang-Bin Huang, P. Saratchandran et al. · IEEE Transactions on Neural Networks Nov 2006 1,985
33 Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture C. L. Philip Chen, Zhulin Liu · IEEE Transactions on Neural Networks and Learning Systems 21 Jul 2017 1,954
34 Extreme learning machines: a survey Guang-Bin Huang, Dian Hui Wang, Yuan Lan · International Journal of Machine Learning and Cybernetics 24 May 2011 1,938
35 On the algorithmic implementation of multiclass kernel-based vector machines Koby Crammer, Yoram Singer Mar 2002 1,780
36 Trends in extreme learning machines: A review Gao Ming Huang, Guang-Bin Huang, Shiji Song et al. · Neural Networks 16 Oct 2014 1,753
37 EMNIST: Extending MNIST to handwritten letters Gregory Cohen, Saeed Afshar, Jonathan C. Tapson et al. Conference paper May 2017 1,680
38 Pegasos: primal estimated sub-gradient solver for SVM Shai Shalev‐Shwartz, Yoram Singer, Nathan Srebro et al. · Mathematical Programming 15 Oct 2010 1,526
39 Regularized multi--task learning Theodoros Evgeniou, Massimiliano Pontil Conference paper 22 Aug 2004 1,491
40 Extreme Learning Machine for Multilayer Perceptron Jiexiong Tang, Chenwei Deng, Guang-Bin Huang · IEEE Transactions on Neural Networks and Learning Systems 7 May 2015 1,478
41 Connectionist learning procedures Geoffrey E. Hinton · Artificial Intelligence Sep 1989 1,465
42 Particle Swarm Optimization: A Comprehensive Survey Tareq M. Shami, Ayman A. El‐Saleh, Mohammed Alswaitti et al. · IEEE Access Open access 2022 1,420
43 Comparing support vector machines with Gaussian kernels to radial basis function classifiers Bernhard Schölkopf, Kah-Kay Sung, Chris J. C. Burges et al. · IEEE Transactions on Signal Processing Open access 1997 1,417
44 Brain tumor classification using deep CNN features via transfer learning S. Deepak, P. M. Ameer · Computers in Biology and Medicine 29 Jun 2019 1,323
45 A Simple Weight Decay Can Improve Generalization Anders Krogh, John Hertz 1992 1,293
46 Large Scale Incremental Learning Yue Wu, Yinpeng Chen, Lijuan Wang et al. Conference paper Jun 2019 1,271
47 PRIME Ping Chi, Shuangchen Li, Cong Xu et al. · ACM SIGARCH Computer Architecture News 18 Jun 2016 1,252
48 Deep Subdomain Adaptation Network for Image Classification Yongchun Zhu, Fuzhen Zhuang, Jindong Wang et al. · IEEE Transactions on Neural Networks and Learning Systems Open access 4 May 2020 1,251
49 Simultaneous Deep Transfer Across Domains and Tasks Eric Tzeng, Judy Hoffman, Trevor J. Darrell et al. Conference paper Dec 2015 1,229
50 Activation functions in deep learning: A comprehensive survey and benchmark Shiv Ram Dubey, Satish Kumar Singh, B.B. Chaudhuri · Neurocomputing 3 Jul 2022 1,226

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.