State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Untitled work

(2014) · Cited by 33,419

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336

Self-Organizing Maps

Kohonen, T. (1995) · Springer series in information sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,032

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001

Internet of Things (IoT): A vision, architectural elements, and future directions

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891

Gaussian Processes for Machine Learning

Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705

Continuous control with deep reinforcement learning

Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782

DeepWalk: online learning of social representations

Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123

Robust Face Recognition via Sparse Representation

Wright, J. N., Yang, A., Ganesh, A., Sastry, S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,398

Q-learning

Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083

An introduction to computing with neural nets

Lippmann, R. P. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,890

Learning Deep Architectures for AI

Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330

Untitled work

(2011) · Cited by 6,993

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878

An efficient k-means clustering algorithm: analysis and implementation

Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017616 · Cited by 5,660

Recurrent neural network based language model

Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390

Curriculum learning

Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1×

A unified architecture for natural language processing: deep neural networks with multitask learning

Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050

Greedy layer-wise training of deep networks

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841

A study of the behavior of several methods for balancing machine learning training data

Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007735 · Cited by 4,446

Untitled work

(2010) · Cited by 4,411

Multi-column deep neural networks for image classification

Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248110 · Cited by 3,880

Bayesian Inference in Statistical Analysis.

Kadane, J. B., Box, G. E. P., & Tiao, G. C. (1975) · Journal of the American Statistical Association · doi:10.2307/2285412 · Cited by 3,853

A survey of decision tree classifier methodology

Safavian, S. R., & Landgrebe, D. A. (1991) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.97458 · Cited by 3,805

Untitled work

(2013) · Cited by 3,740

A Neural Probabilistic Language Model

Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., Jaz, K., Hofmann, T., Poggio, T., & Shawe-Taylor, J. S. (2000) · Cited by 3,451

Deep Reinforcement Learning with Double Q-Learning

Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318

Hierarchical models of object recognition in cortex

Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309

Dueling Network Architectures for Deep Reinforcement Learning

Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06581 · Cited by 3,300

Large Scale Distributed Deep Networks

Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,202

Prioritized Experience Replay

Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv · doi:10.48550/arxiv.1511.05952 · Cited by 3,120

The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

Hochreiter, S. (1998) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488598000094 · Cited by 2,939

Hadoop: The Definitive Guide

White, T. (2009) · Cited by 2,800

Untitled work

(2011) · Cited by 2,752

A neural probabilistic language model

BengioYoshua, DucharmeRéjean, VincentPascal, & JanvinChristian (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944966 · Cited by 2,646

When Is ''Nearest Neighbor'' Meaningful?

Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_15 · Cited by 2,403

Convolutional Neural Networks for Speech Recognition

Abdel‐Hamid, O., Mohamed, A., Jiang, H., Deng, L., Penn, G., & Yu, D. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2339736 · Cited by 2,325

Deep Boltzmann machines

Salakhutdinov, R., & Hinton, G. E. (2009) · Cited by 1,890

Bayesian Inference in Statistical Analysis

Hill, B. M., Box, G. E. P., & Tiao, G. C. (1974) · Technometrics · doi:10.2307/1267683 · Cited by 1,860

A survey of techniques for internet traffic classification using machine learning

Nguyen, T. T. T., & Armitage, G. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080406 · Cited by 1,664

Convolutional Radio Modulation Recognition Networks

O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-44188-7_16 · Cited by 1,620

Self-taught learning

Raina, R., Battle, A. J., Lee, H., Packer, B., & Ng, A. Y. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273592 · Cited by 1,534

Auto-association by multilayer perceptrons and singular value decomposition

Bourlard, H., & Kamp, Y. (1988) · Biological Cybernetics · doi:10.1007/bf00332918 · Cited by 1,391

Generating Text with Recurrent Neural Networks

Sutskever, I., Martens, J., & Hinton, G. E. (2011) · Cited by 1,266

Discriminative K-SVD for dictionary learning in face recognition

Zhang, Q., & Li, B. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539989 · Cited by 1,259

Internet traffic classification using bayesian analysis techniques

Moore, A. W., & Zuev, D. V. (2005) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/1064212.1064220 · Cited by 1,216

A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective

Chen, S., Xu, H., Liu, D., Hu, B., & Wang, H. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2337336 · Cited by 1,212

Discovering regions of different functions in a city using human mobility and POIs

Yuan, J., Zheng, Y., & Xie, X. (2012) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339561 · Cited by 1,118

Connectionist Speech Recognition: A Hybrid Approach

Bourlard, H. A., & Morgan, N. H. (1993) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,115

Multimodal Learning with Deep Boltzmann Machines

Srivastava, N., & Salakhutdinov, R. (2012) · doi:10.5555/2627435.2697059 · Cited by 1,070

Nonnegative Matrix Factorization: A Comprehensive Review

Wang, Y.-X., & Zhang, Y. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.51 · Cited by 983

OSPF Version 2

Moy, J. (1998) · doi:10.17487/rfc2328 · Cited by 971

Machine learning in wireless sensor networks: Algorithms, strategies, and applications

Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965

Support Vector Machines

Steinwart, I., & Christmann, A. (2008) · Information science and statistics · doi:10.1007/978-0-387-77242-4 · Cited by 945

U-Air

Zheng, Y., Liu, F., & Hsieh, H.-P. (2013) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2488188 · Cited by 937

Detection, classification, and tracking of targets

Li, D., Wong, K. D., Hu, Y. H., & Sayeed, A. M. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.985674 · Cited by 934

IEEE Standard for Information technology—Telecommunications and information exchange between systems Local and metropolitan area networks—Specific requirements - Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications

(2016) · doi:10.1109/ieeestd.2016.7786995 · Cited by 929

Principal Component Analysis

(2003) · Technometrics · doi:10.1198/tech.2003.s783 · Cited by 924

Conversational speech transcription using context-dependent deep neural networks

Seide, F. T. B., Li, G., & Yu, D. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-169 · Cited by 877

Parsing with Compositional Vector Grammars

Socher, R., Bauer, J., Manning, C. D., & Ng, A. Y. (2013) · Cited by 830

Learning distributed representations of concepts.

Hinton, G. E. (1989) · eScholarship (California Digital Library) · Cited by 827

Accurate, scalable in-network identification of p2p traffic using application signatures

Sen, S. B., Spatscheck, O., & Wang, D. (2004) · International World Wide Web Conference (WWW) · doi:10.1145/988672.988742 · Cited by 811

Relational learning via latent social dimensions

Tang, L., & Liu, H. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557109 · Cited by 799

Discriminative Unsupervised Feature Learning with Convolutional Neural NetworksPreprint

Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., & Brox, T. (2014) · arXiv (Cornell University) · Cited by 790

Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection

Socher, R., Huang, E., Pennin, J., Manning, C. D., & Ng, A. Y. (2011) · Cited by 785

Classification and clustering via dictionary learning with structured incoherence and shared features

Ramírez, I., Sprechmann, P., & Sapiro, G. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539964 · Cited by 762

A tutorial survey of architectures, algorithms, and applications for deep learning

Deng, L. (2014) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2013.9 · Cited by 747

Large-scale deep unsupervised learning using graphics processors

Raina, R., Madhavan, A., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553486 · Cited by 742

Identifying Mislabeled Training Data

Brodley, C. E., & Friedl, M. A. (1999) · Cited by 718

Traffic classification using clustering algorithms

Erman, J., Arlitt, M. F., & Mahanti, A. (2006) · doi:10.1145/1162678.1162679 · Cited by 712

A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification

Williams, N., Zander, S., & Armitage, G. J. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1163593.1163596 · Cited by 698

Contention avoidance and resolution schemes in bufferless all-optical packet-switched networks: a survey

Rahbar, A. G., & Yang, O. W. W. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080408 · Cited by 676

Human mobility, social ties, and link prediction

Wang, D., Pedreschi, D., Song, C., Giannotti, F., & Barabási, A.-Ĺ. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020581 · Cited by 671

Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription

Seide, F. T. B., Li, G., Xie, C., & Yu, D. (2011) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2011.6163899 · Cited by 648

Identifying Mislabeled Training Data

Brodley, C. E., & Friedl, M. A. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.606 · Cited by 612

Analyzing Peer-To-Peer Traffic Across Large Networks

Sen, S. B., & Wang, J. (2004) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2004.826277 · Cited by 587

Deep Learning via Semi-supervised Embedding

Weston, J., Ratle, F., Mobahi, H., & Collobert, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_34 · Cited by 584

Intelligent Selection of Language Model Training Data

Moore, R. C., & Lewis, W. D. (2010) · Cited by 530

Urban computing with taxicabs

Zheng, Y., Liu, Y., Yuan, J. Y., & Xie, X. (2011) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2030112.2030126 · Cited by 529

A survey of Internet-of-Things: Future vision, architecture, challenges and services

Singh, D., Tripathi, G., & Jara, A. J. (2014) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2014.6803174 · Cited by 511

Advances in optimizing recurrent networks

Bengio, Y., Boulanger-Lewandowski, N., & Pascanu, R. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639349 · Cited by 495

Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification

Roughan, M., Sen, S., Spatscheck, O., & Duffield, N. (2004) · Proceedings of the 4th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1028788.1028805 · Cited by 494

Learning Convolutional Feature Hierarchies for Visual Recognition

Kavukcuoglu, K., Sermanet, P., Boureau, Y.-L., Gregor, K., Mathieu, M., & LeCun, Y. (2010) · Cited by 488

Scaling Learning Algorithms toward AI

Bengio, Y., & LeCun, Y. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7496.003.0016 · Cited by 483

An Efficient Learning Procedure for Deep Boltzmann Machines

Salakhutdinov, R., & Hinton, G. E. (2012) · Neural Computation · doi:10.1162/neco_a_00311 · Cited by 477

Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

Jiang, S., Ferreira, J., & González, M. C. (2016) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2631141 · Cited by 470

Real-time forest fire detection with wireless sensor networks

Yu, L., Wang, N., & Meng, X. (2005) · International Conference on Wireless Communications, Networking and Mobile Computing · doi:10.1109/wcnm.2005.1544272 · Cited by 463

Internet traffic classification using bayesian analysis techniques

Moore, A. W., & Zuev, D. (2005) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1071690.1064220 · Cited by 460

Types of machine learning algorithms

Oladipupo, T. (2010) · New Advances in Machine Learning · doi:10.5772/9385 · Cited by 443

Knowledge-Defined Networking

Mestres, A., Rodriguez-Natal, A., Carner, J., Barlet‐Ros, P., Alarcón, E., Solé, M., Muntés-Mulero, V., Meyer, D., Barkai, S., Hibbett, M. J., et al. (2017) · ACM SIGCOMM Computer Communication Review · doi:10.1145/3138808.3138810 · Cited by 430

Face recognition with support vector machines: global versus component-based approach

Heisele, B., Ho, P., & Poggio, T. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937693 · Cited by 429

A Survey of Self Organisation in Future Cellular Networks

Aliu, O. G., Imran, A., Imran, M. A., & Evans, B. G. (2012) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.021312.00116 · Cited by 411

Deep learning for robust feature generation in audiovisual emotion recognition

Kim, Y., Lee, H., & Provost, E. M. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638346 · Cited by 401

The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective

Kato, N., Fadlullah, Z. M., Mao, B., Tang, F., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2016.1600317wc · Cited by 400

Training recurrent neural networks

Hinton, G. E., & Sutskever, I. (2013) · Cited by 380

Crowd sensing of traffic anomalies based on human mobility and social media

Pan, B., Zheng, Y., Wilkie, D., & Shahabi, C. (2013) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2525314.2525343 · Cited by 377

Leveraging social media networks for classification

Tang, L., & Liu, H. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0210-x · Cited by 360

Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning

Mao, B., Fadlullah, Z. M., Tang, F., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Transactions on Computers · doi:10.1109/tc.2017.2709742 · Cited by 355

Natural Human Mobility Patterns and Spatial Spread of Infectious Diseases

Belik, V., Geisel, T., & Brockmann, D. (2011) · Physical Review X · doi:10.1103/physrevx.1.011001 · Cited by 337

Applications of Machine Learning to Cognitive Radio Networks

Clancy, C., Hecker, J., Stuntebeck, E., & O'Shea, T. (2007) · IEEE Wireless Communications · doi:10.1109/mwc.2007.4300983 · Cited by 328

Understanding urban human activity and mobility patterns using large-scale location-based data from online social media

Hasan, S., Zhan, X., & Ukkusuri, S. V. (2013) · doi:10.1145/2505821.2505823 · Cited by 309

Better Mixing via Deep RepresentationsPreprint

Bengio, Y., Mesnil, G., Dauphin, Y., & Rifai, S. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.4404 · Cited by 297

Online Demand Under Limited Consumer Search

Kim, J. B., Albuquerque, P., & Bronnenberg, B. J. (2010) · Marketing Science · doi:10.1287/mksc.1100.0574 · Cited by 293

Gravity model in the Korean highway

Jung, W., Wang, F. Z., & Stanley, E. (2008) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/81/48005 · Cited by 288

Offline/realtime traffic classification using semi-supervised learning

Erman, J., Mahanti, A., Arlitt, M., Cohen, I., & Williamson, C. (2007) · Performance Evaluation · doi:10.1016/j.peva.2007.06.014 · Cited by 287

Self-organizing maps of symbol strings

Kohonen, T., & Somervuo, P. J. (1998) · Neurocomputing · doi:10.1016/s0925-2312(98)00031-9 · Cited by 283

In-network outlier detection in wireless sensor networks

Branch, J. W., Giannella, C. R., Szymanski, B. K., Wolff, R., & Kargupta, H. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-011-0474-5 · Cited by 272

Neural networks for shortest path computation and routing in computer networks

Ali, M., & Kamoun, F. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.286889 · Cited by 270

Unsupervised learning of distributions on binary vectors using two layer networks

Freund, Y., & Haussler, D. H. (1991) · Cited by 267

Incremental extreme learning machine with fully complex hidden nodes

Huang, G.-B., Li, M.-B., Chen, L., & Siew, C. (2007) · Neurocomputing · doi:10.1016/j.neucom.2007.07.025 · Cited by 259

A Simple Relational Classifier

Macskassy, S. A., & Provost, F. (2003) · doi:10.21236/ada452802 · Cited by 255

Application of sampling methodologies to network traffic characterization

claffy, K., Polyzos, G. C., & Braun, H.-W. (1993) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/166237.166256 · Cited by 243

Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification

Xiao, T., Zhang, J., Yang, K., Peng, Y., & Zhang, Z. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654926 · Cited by 235

Scalable learning of collective behavior based on sparse social dimensions

Tang, L., & Liu, H. (2009) · Proceedings of the 18th ACM conference on Information and knowledge management · doi:10.1145/1645953.1646094 · Cited by 233

Estimating flow distributions from sampled flow statistics

Duffield, N. G., Lund, C., & Thorup, M. (2003) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/863955.863992 · Cited by 230

A Hybrid Malicious Code Detection Method based on Deep Learning

Li, Y., Ma, R., & Jiao, R. (2015) · International Journal of Security and Its Applications · doi:10.14257/ijsia.2015.9.5.21 · Cited by 229

Feature Learning in Deep Neural Networks - Studies on Speech Recognition TasksPreprint

Yu, D., Seltzer, M. L., Li, J., Huang, J.-T., & Seide, F. T. B. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3605 · Cited by 228

Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning

Chen, C. L. P., Zhang, C.-Y., Chen, L., & Gan, M. (2015) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2015.2406889 · Cited by 223

Localization In Wireless Sensor Networks Based on Support Vector Machines

Tran, D. A., & Nguyen, T. X. (2008) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2007.70800 · Cited by 210

Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines

Kaplantzis, S., Shilton, A., Mani, N., & Şekercioğlu, Y. A. (2007) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2007.4496866 · Cited by 208

Global optimization of a neural network-hidden Markov model hybrid

Bengio, Y., De Mori, R., Flammia, G., & Kompe, R. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.125866 · Cited by 203

A handwritten character recognition system using directional element feature and asymmetric Mahalanobis distance

Kato, N., Suzuki, M., Omachi, S., Aso, H., & NEMOTO, Y. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.754617 · Cited by 191

Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks

Rajasegarar, S., Leckie, C. A., Palaniswami, M. S., & Bezdek, J. C. (2007) · International Conference on Communications · doi:10.1109/icc.2007.637 · Cited by 177

Quantifying the semantics of search behavior before stock market moves

Curme, C., Preis, T., Stanley, E., & Moat, H. S. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1324054111 · Cited by 175

Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks

Janakiram, D., Kumar, A. V. U. P., & Reddy, A. M. (2006) · COMmunication System softWAre and MiddlewaRE · doi:10.1109/comswa.2006.1665221 · Cited by 164

iMouse: An Integrated Mobile Surveillance and Wireless Sensor System

Tseng, Y., Wang, Y., Cheng, K.-Y., & Hsieh, Y.-Y. (2007) · Computer · doi:10.1109/mc.2007.211 · Cited by 164

Online Incremental Feature Learning with Denoising Autoencoders

Zhou, G., Sohn, K., & Lee, H. (2012) · Cited by 162

Support vector machines

Fridrich, J. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9781139192903.019 · Cited by 159

Deep Learning for Efficient Discriminative Parsing

Collobert, R. (2011) · Cited by 158

Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home

Lu, C.-H., & Fu, L. (2009) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2009.2021981 · Cited by 155

A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs

Wang, P., Lin, S.-C., & Luo, M. (2016) · 2016 IEEE International Conference on Services Computing (SCC) · doi:10.1109/scc.2016.133 · Cited by 155

Measuring social functions of city regions from large-scale taxi behaviors

Qi, G., Li, X., Li, S., Pan, G., Wang, Z., & Zhang, D. (2011) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2011.5766912 · Cited by 153

Deep Learning for NLP (without Magic)

Socher, R., Bengio, Y., & Manning, C. D. (2012) · Cited by 150

Face Recognition with Support Vector Machines: Global versus Component-Based Approach

Heisele, B., Ho, P., & Poggio, T. (2001) · doi:10.21236/ada459707 · Cited by 149

Self-Supervised Visual Descriptor Learning for Dense Correspondence

Schmidt, T., Newcombe, R., & Fox, D. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2634089 · Cited by 148

Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection

Moustapha, A. I., & Šelmić, R. R. (2008) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2007.913803 · Cited by 147

Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Abbas, N., Nasser, Y., & Ahmad, K. E. (2015) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-015-0381-7 · Cited by 143

Mobility prediction-based smartphone energy optimization for everyday location monitoring

Chon, Y., Talipov, E., Shin, H. J., & Cha, H. (2011) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2070942.2070952 · Cited by 141

Traffic Classification Using a Statistical Approach

Zuev, D., & Moore, A. W. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-31966-5_25 · Cited by 140

Time series models for internet traffic

Basu, S., Mukherjee, A., & Klivansky, S. M. (1996) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.1996.493355 · Cited by 140

Modeling word perception using the Elman network

Liou, C., Huang, J.-C., & Yang, W.-C. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.04.030 · Cited by 138

Selecting Receptive Fields in Deep Networks

Coates, A. (2024) · doi:10.57702/4h3zav33 · Cited by 138

Urban human mobility data mining: An overview

Zhao, K., Tarkoma, S., Liu, S., & Vo, H. T. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840811 · Cited by 136

Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine

Zhang, Y., Meratnia, N., & Havinga, P. (2012) · Ad Hoc Networks · doi:10.1016/j.adhoc.2012.11.001 · Cited by 132

Many-Layered Learning

Utgoff, P. E., & Stracuzzi, D. J. (2002) · Neural Computation · doi:10.1162/08997660260293319 · Cited by 129

Deep and Wide: Multiple Layers in Automatic Speech Recognition

Morgan, N. H. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2116010 · Cited by 128

Training on multiple sub-flows to optimise the use of Machine Learning classifiers in real-world IP networks

Thu, T. N. T., & Armitage, G. J. (2006) · Conference on Local Computer Networks · doi:10.1109/lcn.2006.322122 · Cited by 123

Toward Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks

Takaishi, D., Nishiyama, H., Kato, N., & Miura, R. (2014) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2014.2318177 · Cited by 123

Generating multi-fingered robotic grasps via deep learning

Varley, J., Weisz, J., Weiss, J., & Allen, P. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354004 · Cited by 121

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

Zorzi, M., Zanella, A., Testolin, A., Grazia, M. D. F. D., & Zorzi, M. (2015) · IEEE Access · doi:10.1109/access.2015.2471178 · Cited by 120

Self-Learning IP Traffic Classification Based on Statistical Flow Characteristics

Zander, S., Thu, T. N. T., & Armitage, G. J. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-31966-5_26 · Cited by 116

Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks

Mancini, M., Costante, G., Valigi, P., & Ciarfuglia, T. A. (2016) · 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759632 · Cited by 110

Extending the lifetime of wireless sensor networks: A hybrid routing algorithm

Abdulla, A. E. A. A., Nishiyama, H., & Kato, N. (2011) · Computer Communications · doi:10.1016/j.comcom.2011.10.001 · Cited by 108

Identification and Prediction of Internet Traffic Using Artificial Neural Networks

Chabaa, S., Zeroual, A., & Antari, J. (2010) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2010.23018 · Cited by 102

Predicting link quality using supervised learning in wireless sensor networks

Wang, Y., Martonosi, M., & Peh, L.-S. (2007) · ACM SIGMOBILE Mobile Computing and Communications Review · doi:10.1145/1317425.1317434 · Cited by 101

Modification of the gravity model and application to the metropolitan Seoul subway system

Goh, S., Lee, K., Park, J. S., & Choi, M. Y. (2012) · Physical Review E · doi:10.1103/physreve.86.026102 · Cited by 92

HYMN: A Novel Hybrid Multi-Hop Routing Algorithm to Improve the Longevity of WSNs

Abdulla, A. E. A. A., Nishiyama, H., Yang, J., Ansari, N., & Kato, N. (2012) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2012.050112.111222 · Cited by 91

Target Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks

Kim, W., Park, J., Yoo, J., Kim, H. J., & Park, C. G. (2012) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2226151 · Cited by 89

Data Acquisition through Joint Compressive Sensing and Principal Component Analysis

Masiero, R., Quer, G., Munaretto, D., Rossi, M., Widmer, J., & Zorzi, M. (2009) · Globecom · doi:10.1109/glocom.2009.5425458 · Cited by 88

Neural network based secure media access control protocol for wireless sensor networks

Kulkarni, R. V., & Venayagamoorthy, G. K. (2009) · doi:10.1109/ijcnn.2009.5179075 · Cited by 87

Fault-resilient sensing in wireless sensor networks

Nakayama, H., Ansari, N., Jamalipour, A., & Kato, N. (2007) · Computer Communications · doi:10.1016/j.comcom.2007.04.023 · Cited by 86

On Minimizing the Impact of Mobility on Topology Control in Mobile Ad Hoc Networks

Nishiyama, H., Ngo, T., Ansari, N., & Kato, N. (2012) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2012.010312.110783 · Cited by 85

Real-time deep learning of robotic manipulator inverse dynamics

Polydoros, A., Nalpantidis, L., & Krüger, V. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353857 · Cited by 84

Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation

Schwenk, H., Rousseau, A., & Attik, M. (2012) · HAL · Cited by 84

Self-optimization of capacity and coverage in LTE networks using a fuzzy reinforcement learning approach

Razavi, R., Klein, S., & Claußen, H. (2010) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc.2010.5671622 · Cited by 81

FROMS: Feedback Routing for Optimizing Multiple Sinks in WSN with Reinforcement Learning

Forster, A., & Murphy, A. L. (2007) · 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information · doi:10.1109/issnip.2007.4496872 · Cited by 81

Effective Data Collection Via Satellite-Routed Sensor System (SRSS) to Realize Global-Scaled Internet of Things

Kawamoto, Y., Nishiyama, H., Fadlullah, Z. M., & Kato, N. (2013) · IEEE Sensors Journal · doi:10.1109/jsen.2013.2262676 · Cited by 80

A Novel Scheme for WSAN Sink Mobility Based on Clustering and Set Packing Techniques

Nakayama, H., Fadlullah, Z. M., Ansari, N., & Kato, N. (2011) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2163872 · Cited by 80

Training Recurrent Neural Networks

Sutskever, I. (2013) · Cited by 79

A Novel Embedding Method for Information Diffusion Prediction in Social Network Big Data

Gao, S., Huacan, P., Gallinari, P., Guo, J. H., & Kato, N. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2684160 · Cited by 76

A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation

Yu, J., Weng, K., Liang, G., & Xie, G. (2013) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2013.6739623 · Cited by 75

A fuzzy reinforcement learning approach for self-optimization of coverage in LTE networks

Razavi, R., Klein, S., & Claußen, H. (2010) · Bell Labs Technical Journal · doi:10.1002/bltj.20463 · Cited by 73

Intrusion detection system (IDS) for combating attacks against cognitive radio networks

Fadlullah, Z. M., Nishiyama, H., Kato, N., & Fouda, M. M. (2013) · IEEE Network · doi:10.1109/mnet.2013.6523809 · Cited by 72

Localization Using Neural Networks in Wireless Sensor Networks

Shareef, A., Zhu, Y., & Musavi, M. T. (2008) · doi:10.4108/icst.mobilware2008.2901 · Cited by 71

mmdump

der Merwe, J. V., Cáceres, R., Chu, Y.-H., & Sreenan, C. J. (2000) · ACM SIGCOMM Computer Communication Review · doi:10.1145/505672.505678 · Cited by 71

Reinforcement learning strategies for self-organized coverage and capacity optimization

Islam, M. N. U., & Mitschele‐Thiel, A. (2012) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2012.6214281 · Cited by 69

Behavioral authentication of server flows

Early, J. P., Brodley, C. E., & Rosenberg, C. (2004) · Annual Computer Security Applications Conference · doi:10.1109/csac.2003.1254309 · Cited by 68

Reducing the data transmission in Wireless Sensor Networks using the Principal Component Analysis

Rooshenas, A., Rabiee, H. R., Movaghar, A., & Naderi, M. Y. (2010) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2010.5706781 · Cited by 68

Neural network implementation of the shortest path algorithm for traffic routing in communication networks

Zhang, L., & Thomopoulos, S. C. A. (1989) · doi:10.1109/ijcnn.1989.118375 · Cited by 66

Impact of travel patterns on epidemic dynamics in heterogeneous spatial metapopulation networks

Ni, S., & Weng, W. (2009) · Physical Review E · doi:10.1103/physreve.79.016111 · Cited by 65

Q-Probabilistic Routing in Wireless Sensor Networks

Arroyo-Valles, R., Alaiz-Rodriguez, R., Guerrero-Curieses, A., & Cid-Sueiro, J. (2007) · 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information · doi:10.1109/issnip.2007.4496810 · Cited by 65

Consensus-based distributed principal component analysis in wireless sensor networks

Macua, S. V., Belanović, P., & Zazo, S. (2010) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc.2010.5671089 · Cited by 64

Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network

SHEN, Y., & Wang, M. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.10.024 · Cited by 61

An Infinite Restricted Boltzmann Machine

Côté, M.-A., & Larochelle, H. (2016) · Neural Computation · doi:10.1162/neco_a_00848 · Cited by 60

Reinforcement Learning Based Geographic Routing Protocol for UWB Wireless Sensor Network

Dong, S., Agrawal, P., & Sivalingam, K. M. (2007) · doi:10.1109/glocom.2007.127 · Cited by 59

Traffic sign recognition with convolutional neural network based on max pooling positions

Qian, R., Yue, Y., Coenen, F., & Zhang, B. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2016.7603237 · Cited by 58

An Energy-Efficient and Delay-Aware Wireless Computing System for Industrial Wireless Sensor Networks

Suto, K., Nishiyama, H., Kato, N., & Huang, C. (2015) · IEEE Access · doi:10.1109/access.2015.2443171 · Cited by 57

A cooperative Reinforcement Learning approach for Inter-Cell Interference Coordination in OFDMA cellular networks

Dirani, M., & Altman, Z. (2010) · Cited by 56

Autonomic network management principles : from concepts to applications

Agoulmine, N. (2011) · Cited by 52

Object identification from few examples by improving the invariance of a Deep Convolutional Neural Network

Pasquale, G., Ciliberto, C., Rosasco, L., & Natale, L. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759720 · Cited by 51

An online outlier detection technique for wireless sensor networks using unsupervised quarter-sphere support vector machine

Zhang, Y., Meratnia, N., & Havinga, P. (2008) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2008.4761978 · Cited by 50

Large-Scale Feature Learning With Spike-and-Slab Sparse CodingPreprint

Goodfellow, I., Courville, A. C., & Bengio, Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.6407 · Cited by 49

Deep Learning Applied to Mobile Phone Data for Individual Income Classification

Sundsøy, P., Bjelland, J., Reme, B., M.Iqbal, A., & Jahani, E. (2016) · International Conference on Artificial Intelligence Technologies and Applications (ICAITA) · doi:10.2991/icaita-16.2016.24 · Cited by 48

A Bayesian analysis of Compressive Sensing data recovery in Wireless Sensor Networks

Masiero, R., Quer, G., Rossi, M., & Zorzi, M. (2009) · International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) · doi:10.1109/icumt.2009.5345599 · Cited by 47

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong, N. C., Hoang, D. T., Gong, S., Niyato, D. T., Wang, P., Liang, Y., & Kim, D. I. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2916583 · Cited by 2,181

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,913

Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471

An Intelligent Routing Framework for High- Traffic Networks using Deep Learning

Lakshman, V., & Sunkara, B. (2025) · International Journal of Innovative Research in Computer and Communication Engineering · doi:10.15680/ijircce.2025.1303002 · Cited by 2

The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective

Kato, N., Fadlullah, Z. M., Mao, B., Tang, F., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2016.1600317wc · Cited by 400

On Intelligent Traffic Control for Large-Scale Heterogeneous Networks: A Value Matrix-Based Deep Learning Approach

Fadlullah, Z. M., Tang, F., Mao, B., Liu, J., & Kato, N. (2018) · IEEE Communications Letters · doi:10.1109/lcomm.2018.2875431 · Cited by 52

Deep Reinforcement Learning For Intelligent Network Traffic Control

Akhaury, A. (2025) · Minerva Access (University of Melbourne)

Artificial intelligence based learning for wireless application – A survey

Raja, L., Velmurugan, S., Shanthi, G., & Nirmala, S. (2022) · AIP conference proceedings · doi:10.1063/5.0072711 · Cited by 3