survey

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey

Authors:

Devki Nandan Jha,

Albert Y. Zomaya,

Rajiv RanjanAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 4

Article No.: 82, Pages 1 - 47

https://doi.org/10.1145/3398020

Published: 03 August 2020 Publication History

Abstract

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages.

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References

[1]

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI’16). 265--283.

Digital Library

[2]

Stefan Achleitner, Thomas La Porta, Trent Jaeger, and Patrick McDaniel. 2017. Adversarial network forensics in software defined networking. In Proceedings of the Symposium on SDN Research. ACM, 8--20.

[3]

Alham Fikri Aji and Kenneth Heafield. 2017. Sparse communication for distributed gradient descent. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’17). 440--445.

[4]

Furqan Alam, Rashid Mehmood, Iyad Katib, Nasser N. Albogami, and Aiiad Albeshri. 2017. Data fusion and IoT for smart ubiquitous environments: A survey. IEEE Access 5 (2017), 9533--9554.

[5]

Suad A. Alasadi and Wesam S. Bhaya. 2017. Review of data preprocessing techniques in data mining. J. Eng. Appl. Sci. 12, 16 (2017), 4102--4107.

[6]

Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. 2017. QSGD: Communication-efficient SGD via gradient quantization and encoding. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’17). 1709--1720.

[7]

Fayçal Ait Aoudia, Matthieu Gautier, and Olivier Berder. 2018. RLMan: An energy manager based on reinforcement learning for energy harvesting wireless sensor networks. IEEE Trans. Green Commun. Netw. 2, 2 (2018), 408–417.

[8]

Jeremy Appleyard, Tomas Kocisky, and Phil Blunsom. 2016. Optimizing performance of recurrent neural networks on GPUs. arXiv preprint arXiv:1604.01946 (2016).

[9]

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. 2017. A brief survey of deep reinforcement learning. arXiv preprint arXiv:1708.05866 (2017).

[10]

Giuseppe Ateniese, Giovanni Felici, Luigi V. Mancini, Angelo Spognardi, Antonio Villani, and Domenico Vitali. 2013. Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers. arXiv (2013).

[11]

Stéphane Ayache and Georges Quénot. 2007. Evaluation of active learning strategies for video indexing. Signal Process.: Image Commun. 22, 7–8 (2007), 692--704.

[12]

Jimmy Ba, Roger Grosse, and James Martens. 2016. Distributed second-order optimization using Kronecker-factored approximations. In 5th International Conference on Learning Representations Toulon, France, April 24-26, 2017, Conference Track Proceedings. https://openreview.net/forum?id=SkkTMpjex.

[13]

Bowen Baker, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik. 2017. Accelerating neural architecture search using performance prediction. arXiv preprint arXiv:1705.10823 (2017).

[14]

Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy, and Max Welling. 2015. Bayesian dark knowledge. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’15). 3438--3446.

[15]

Pierre Baldi. 2012. Autoencoders, unsupervised learning, and deep architectures. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning. 37--49.

Digital Library

[16]

Roberto Battiti. 1994. Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Netw. 5, 4 (1994), 537--550.

Digital Library

[17]

Amir Beck, Angelia Nedić, Asuman Ozdaglar, and Marc Teboulle. 2014. An o(1/k) gradient method for network resource allocation problems. IEEE Trans. Control Netw. Syst. 1, 1 (2014), 64--73.

[18]

Tal Ben-Nun, Ely Levy, Amnon Barak, and Eri Rubin. 2015. Memory access patterns: The missing piece of the multi-GPU puzzle. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC’15). IEEE, 1--12.

Digital Library

[19]

Gyora M. Benedek and Alon Itai. 1991. Learnability with respect to fixed distributions. Theoret. Comput. Sci. 86, 2 (1991), 377--389.

Digital Library

[20]

Yoshua Bengio. 2012. Practical recommendations for gradient-based training of deep architectures. In Neural Networks: Tricks of the Trade. Springer, 437--478.

Digital Library

[21]

Josep Ll Berral, Íñigo Goiri, Ramón Nou, Ferran Julià, Jordi Guitart, Ricard Gavaldà, and Jordi Torres. 2010. Towards energy-aware scheduling in data centers using machine learning. In Proceedings of the Conference on E-Energy. ACM, 215--224.

Digital Library

[22]

Behnaz Bigdeli and Peter Reinartz. 2015. Fusion of hyperspectral and LIDAR data using decision template-based fuzzy multiple classifier system. Int. J. Appl. Earth Obs. 38 (2015), 309–320.

[23]

Battista Biggio, Blaine Nelson, and Pavel Laskov. 2012. Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389 (2012).

Digital Library

[24]

Battista Biggio, Konrad Rieck, Davide Ariu, Christian Wressnegger, Igino Corona, Giorgio Giacinto, and Fabio Roli. 2014. Poisoning behavioral malware clustering. In Proceedings of the Workshop on Artificial Intelligence and Security Workshop. ACM, 27--36.

Digital Library

[25]

Farshid Hassani Bijarbooneh, Wei Du, Edith C.-H. Ngai, Xiaoming Fu, and Jiangchuan Liu. 2016. Cloud-assisted data fusion and sensor selection for internet of things. IEEE Internet Things J. 3, 3 (2016), 257--268.

[26]

Tewodros A. Biresaw, Andrea Cavallaro, and Carlo S. Regazzoni. 2015. Tracker-level fusion for robust Bayesian visual tracking. IEEE Trans. Circ. Syst. Video Technol. 25, 5 (2015).

Digital Library

[27]

Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A. Lozano. 2020. A review on outlier/anomaly detection in time series data. arXiv preprint arXiv:2002.04236 (2020).

[28]

Matthias Boehm, Michael W. Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Arvind C. Surve, et al. 2016. Systemml: Declarative machine learning on spark. Proc. VLDB Endow. 9, 13 (2016), 1425--1436.

Digital Library

[29]

Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H. Brendan McMahan, et al. 2019. Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 (2019).

[30]

Keith Bonawitz, Fariborz Salehi, Jakub Konečnỳ, Brendan McMahan, and Marco Gruteser. 2019. Federated learning with autotuned communication-efficient secure aggregation. arXiv preprint arXiv:1912.00131 (2019).

[31]

Leon Bottou. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of the International Conference on Computational Statistics (COMPSTAT’10). Springer, 177--186.

[32]

Léon Bottou. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade. Springer, 421--436.

[33]

Léon Bottou, Frank E. Curtis, and Jorge Nocedal. 2018. Optimization methods for large-scale machine learning. SIAM Rev. 60, 2 (2018), 223--311.

[34]

Olivier Bousquet, Stephane Boucheron, and Gabor Lugosi. 2003. Introduction to statistical learning theory. In Proceedings of the Summer School on Machine Learning. Springer, 169--207.

[35]

Joseph K Bradley, Aapo Kyrola, Danny Bickson, and Carlos Guestrin. 2011. Parallel coordinate descent for l1-regularized loss minimization. arXiv preprint arXiv:1105.5379 (2011).

[36]

Leo Breiman. 2001. Random forests. Mach. Learn. 45, 1 (2001), 5--32.

Digital Library

[37]

Leo Breiman. 2017. Classification and Regression Trees. Routledge.

[38]

Uwe Breitenbucher, Kalman Kepes, Frank Leymann, and Michael Wurster. 2017. Declarative vs. imperative: How to model the automated deployment of IoT applications? InProceedings of the Summer School on Service Oriented Computing (SummerSOC’17), 18--27.

[39]

Andrew Brock, Theodore Lim, James M. Ritchie, and Nick Weston. 2017. SMASH: One-shot model architecture search through hypernetworks. arXiv preprint arXiv:1708.05344 (2017).

[40]

Richard H. Byrd, Samantha L. Hansen, Jorge Nocedal, and Yoram Singer. 2016. A stochastic quasi-Newton method for large-scale optimization. SIAM J. Optimiz. 26, 2 (2016), 1008--1031.

[41]

Benjamin Cabé, Eclipse IoT Working Group, et al. 2018. IoT developer survey 2018. SlideShare, April 13 (2018).

[42]

Miguel Castro, Barbara Liskov, et al. 1999. Practical Byzantine fault tolerance. In Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI’99), Vol. 99. 173--186.

[43]

Z. Berkay Celik, Leonardo Babun, Amit Kumar Sikder, Hidayet Aksu, Gang Tan, Patrick McDaniel, and A. Selcuk Uluagac. 2018. Sensitive information tracking in commodity IoT. In Proceedings of the 27th USENIX Security Symposium (USENIXSecurity’18). 1687--1704.

[44]

Nitesh V. Chawla, Nathalie Japkowicz, and Aleksander Kotcz. 2004. Special issue on learning from imbalanced data sets. ACM SIGKDD Explor. Newslett. 6, 1 (2004), 1--6.

Digital Library

[45]

Kumar Chellapilla, Sidd Puri, and Patrice Simard. 2006. High performance convolutional neural networks for document processing. In Proceedings of the International Conference on Frontiers in Handwriting Recognition (IWFHR’06). Suvisoft.

[46]

Chi-Chung Chen, Chia-Lin Yang, and Hsiang-Yun Cheng. 2018. Efficient and robust parallel DNN training through model parallelism on multi-GPU platform. arXiv preprint arXiv:1809.02839 (2018).

[47]

Jianmin Chen, Xinghao Pan, Rajat Monga, Samy Bengio, and Rafal Jozefowicz. 2016. Revisiting distributed synchronous SGD. arXiv preprint arXiv:1604.00981 (2016).

[48]

Lei Chen, Jiwen Lu, Zhanjie Song, and Jie Zhou. 2018. Part-activated deep reinforcement learning for action prediction. In Proceedings of the European Conference on Computer Vision (ECCV’18). 421--436.

[49]

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. In Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI’18). 578--594.

[50]

Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song. 2017. Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017).

[51]

Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. 2017. A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282 (2017).

[52]

Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer. 2014. cudnn: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759 (2014).

[53]

Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman. 2014. Project adam: Building an efficient and scalable deep learning training system. In Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI’14). 571--582.

Digital Library

[54]

Ching-Tai Chin, Kishan Mehrotra, Chilukuri K. Mohan, S. Rankat, et al. 1994. Training techniques to obtain fault-tolerant neural networks. In Proceedings of the International Symposium on Fault-Tolerant Computing (FTCS’94). IEEE, 360--369.

[55]

C.-T. Chiu, Kishan Mehrotra, Chilukuri K. Mohan, and Sanjay Ranka. 1993. Robustness of feedforward neural networks. In Proceedings of the IEEE International Conference on Neural Networks. IEEE, 783--788.

[56]

François Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 1251--1258.

[57]

Praveen Chopra and Sandeep Kumar Yadav. 2015. Fault detection and classification by unsupervised feature extraction and dimensionality reduction. Complex Intell. Systems 1, 1–4 (2015), 25–33.

[58]

Cheng-Tao Chu, Sang K. Kim, Yi-An Lin, YuanYuan Yu, Gary Bradski, Kunle Olukotun, and Andrew Y. Ng. 2007. Map-reduce for machine learning on multicore. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’ (2007). 281--288.

[59]

L.-C. Chu and Benjamin W. Wah. 1990. Fault tolerant neural networks with hybrid redundancy. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’90). IEEE.

[60]

Igor Colin, Ludovic Dos Santos, and Kevin Scaman. 2019. Theoretical limits of pipeline parallel optimization and application to distributed deep learning. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’19). 12350--12359.

[61]

Graham Cormode, Minos Garofalakis, Peter J. Haas, Chris Jermaine, et al. 2011. Synopses for massive data: Samples, histograms, wavelets, sketches. Found. Trends Databases 4, 1–3 (2011), 1--294.

Digital Library

[62]

NVIDIA Corporation. 2015. NVIDIA Collective Communications Library (NCCL). Retrieved from https://developer.nvidia.com/nccl.

[63]

Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Mach. Learn. 20, 3 (1995), 273--297.

[64]

Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv (2016).

[65]

Daniel Crankshaw, Gur-Eyal Sela, Corey Zumar, Xiangxi Mo, Joseph E. Gonzalez, Ion Stoica, and Alexey Tumanov. 2018. InferLine: ML inference pipeline composition framework. arXiv preprint arXiv:1812.01776 (2018).

[66]

Daniel Crankshaw, Xin Wang, Guilio Zhou, Michael J. Franklin, Joseph E. Gonzalez, and Ion Stoica. 2017. Clipper: A low-latency online prediction serving system. In Proceedings of the 14th USENIX Symposium on NSDI. 613--627.

[67]

Henggang Cui, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, and Eric P. Xing. 2016. Geeps: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server. In Proceedings of the European Conference on Computer Systems (EuroSys’16). 1--16.

[68]

Ana Cristina Franco da Silva, Uwe Breitenbücher, Pascal Hirmer, Kálmán Képes, Oliver Kopp, Frank Leymann, Bernhard Mitschang, and Ronald Steinke. 2017. Internet of Things out of the box: Using TOSCA for automating the deployment of IoT environments. In Proceedings of the International Conference on Cloud Computing and Services Science (CLOSER’17). 330--339.

Digital Library

[69]

Manoranjan Dash and Huan Liu. 2003. Consistency-based search in feature selection. Artific. Intell. 151, 1–2 (2003), 155--176.

Digital Library

[70]

Roberto Souto Maior de Barros and Silas Garrido T. de Carvalho Santos. 2019. An overview and comprehensive comparison of ensembles for concept drift. Info. Fusion 52 (2019), 213--244.

[71]

Christopher M De Sa, Ce Zhang, Kunle Olukotun, and Christopher Ré. 2015. Taming the wild: A unified analysis of hogwild-style algorithms. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’15). 2674--2682.

[72]

Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V. Le, et al. 2012. Large scale distributed deep networks. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’12). 1223--1231.

[73]

Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao. 2012. Optimal distributed online prediction using mini-batches. J. Mach. Learn. Res. 13 (Jan. 2012), 165--202.

[74]

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 248--255.

[75]

Suguna Devi and T. Neetha. 2017. Machine learning-based traffic congestion prediction in a IoT-based smart city. Int. Res. J. Eng. Technol. 4 (2017), 3442–3445.

[76]

Greg Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, and Sanjeev Satheesh. 2016. Persistent rnns: Stashing recurrent weights on-chip. In Proceedings of the International Conference on Machine Learning. 2024--2033.

[77]

Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, and Min Sun. 2018. Dpp-net: Device-aware progressive search for pareto-optimal neural architectures. In Proceedings of the European Conference on Computer Vision (ECCV’18). 517--531.

[78]

Wei Dong, Chun Chen, Jiajun Bu, Xue Liu, and Yunhao Liu. 2013. D2: Anomaly detection and diagnosis in networked embedded systems by program profiling and symptom mining. In Proceedings of the IEEE Real-Time Systems Symposium (RTSS’13). IEEE, 202--211.

Digital Library

[79]

Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, and Brian Van Essen. 2019. Improving strong-scaling of CNN training by exploiting finer-grained parallelism. In Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS’19). IEEE, 210--220.

[80]

Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, and Brian Van Essen. 2019. Channel and filter parallelism for large-scale CNN training. In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’19). 1--20.

Digital Library

[81]

Nikoli Dryden, Tim Moon, Sam Ade Jacobs, and Brian Van Essen. 2016. Communication quantization for data-parallel training of deep neural networks. In Proceedings of the 2nd Workshop on Machine Learning in HPC Environments (MLHPC’16). IEEE, 1--8.

[82]

John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12 (July 2011), 2121--2159.

Digital Library

[83]

Santiago Egea, Albert Rego Mañez, Belen Carro, Antonio Sánchez-Esguevillas, and Jaime Lloret. 2017. Intelligent IoT traffic classification using novel search strategy for fast-based-correlation feature selection in industrial environments. IEEE Internet Things J. 5, 3 (2017), 1616--1624.

[84]

Anne Elk. 2019. Distributed Machine Learning Toolkit: Big Data, Big Model, Flexibility, Efficiency. Retrieved from http://www.dmtk.io.

[85]

Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2018. Multi-objective architecture search for cnns. arXiv preprint arXiv:1804.09081 2 (2018).

[86]

Martin D. Emmerson et al. 1993. Determining and improving the fault tolerance of multilayer perceptrons in a pattern-recognition application. IEEE Trans. Neural Netw. 4, 5 (1993), 788–793.

Digital Library

[87]

Tugba Erpek, Yalin E. Sagduyu, and Yi Shi. 2018. Deep learning for launching and mitigating wireless jamming attacks. TCCN (2018).

[88]

Sharanya Eswaran, Archan Misra, Flavio Bergamaschi, and Thomas La Porta. 2012. Utility-based bandwidth adaptation in mission-oriented wireless sensor networks. TOSN 8, 2 (2012), 1–26.

Digital Library

[89]

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. 2010. The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88, 2 (June 2010), 303--338.

Digital Library

[90]

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. 2019. Deep learning for time series classification: A review. Data Min. Knowl. Discov. 33, 4 (2019), 917--963.

Digital Library

[91]

George Frewat, Charbel Baroud, Roy Sammour, Abdallah Kassem, and Mustapha Hamad. 2016. Android voice recognition application with multi speaker feature. In Proceedings of the 18th IEEE Mediterranean Electrotechnical Conference (MELECON’16). IEEE, 1--5.

[92]

Colleen E. Fuss, Aaron A. Berg, and John B. Lindsay. 2016. DEM Fusion using a modified k-means clustering algorithm. Int. J. Dig. Earth 9, 12 (2016), 1242--1255.

[93]

João Gama, Indrundefined Žliobaitundefined, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A survey on concept drift adaptation. ACM Comput. Surv. 46, 4 (Mar. 2014).

Digital Library

[94]

Peter Garraghan, Renyu Yang, Zhenyu Wen, Alexander Romanovsky, Jie Xu, Rajkumar Buyya, and Rajiv Ranjan. 2018. Emergent failures: Rethinking cloud reliability at scale. IEEE Cloud Comput. 5, 5 (2018), 12--21.

[95]

Alexander L. Gaunt, Matthew A. Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, and Sam Webster. 2017. AMPNet: Asynchronous model-parallel training for dynamic neural networks. arXiv preprint arXiv:1705.09786 (2017).

[96]

Amir Gholami, Ariful Azad, Peter Jin, Kurt Keutzer, and Aydin Buluc. 2018. Integrated model, batch, and domain parallelism in training neural networks. In Proceedings of the ACM Symposium on Parallel Algorithms and Architectures (SPAA’18). 77--86.

Digital Library

[97]

R. A. Gilyazev and D. Yu Turdakov. 2018. Active learning and crowdsourcing: A survey of optimization methods for data labeling. Prog. Comput. Soft. 44, 6 (2018), 476–491.

Digital Library

[98]

David E. Goldberg. 1989. Genetic Algorithms in Search, Optimization and Machine Learning (1st ed.). Addison-Wesley Longman Publishing, Boston, MA.

Digital Library

[99]

Heitor M. Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfharinger, Geoff Holmes, and Talel Abdessalem. 2017. Adaptive random forests for evolving data stream classification. Mach. Learn. 106, 9-10 (2017), 1469--1495.

Digital Library

[100]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’14). 2672--2680.

[101]

Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).

[102]

Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. 2017. Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 (2017).

[103]

Jorge Granjal, Edmundo Monteiro, and Jorge Sá Silva. 2010. A secure interconnection model for IPv6 enabled wireless sensor networks. In Proceedings of the International Federation for Information Processing Wireless Days (IFIP’10). IEEE, 1--6.

[104]

Alex Graves, Abdel Rahman Mohamed, and Geoffrey E. Hinton. 2013. Speech recognition with deep recurrent neural networks. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP’13). 6645--6649.

[105]

Maciej Grzenda, Heitor Murilo Gomes, and Albert Bifet. 2019. Delayed labelling evaluation for data streams. Data Min. Knowl. Discov. (2019), 1--30.

[106]

Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg. 2017. Badnets: Identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733 (2017).

[107]

Lei Guan, Wotao Yin, Dongsheng Li, and Xicheng Lu. 2019. XPipe: Efficient pipeline model parallelism for multi-GPU DNN training. arXiv preprint arXiv:1911.04610 (2019).

[108]

Yu Guan and Thomas Plötz. 2017. Ensembles of deep lstm learners for activity recognition using wearables. Interact. Mobile Wear. Ubiq. Technol. 1, 2 (2017), 11.

Digital Library

[109]

Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. 2015. Deep learning with limited numerical precision. In Proceedings of the International Conference on Machine Learning (ICML’15). 1737--1746.

[110]

Ricardo Gutierrez-Osuna. 2002. Pattern analysis for machine olfaction: A review. IEEE Sensors journal (2002).

[111]

Hamed Haddad, Ali Dehghan, Raouf Khayami, and Kim-Kwang Raymond Choo. 2018. A deep Recurrent Neural Network-based approach for Internet of Things malware threat hunting. Future Gen. Comput. Syst. 85 (2018), 88–96.

[112]

Mark A. Hall. 2000. Correlation-based feature selection of discrete and numeric class machine learning. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML'00). Morgan Kaufmann Publishers Inc., 359–366.

[113]

Yi Han, David Hubczenko, Paul Montague, Olivier De Vel, Tamas Abraham, Benjamin IP Rubinstein, Christopher Leckie, Tansu Alpcan, and Sarah Erfani. 2019. Adversarial reinforcement learning under partial observability in software-defined networking. arXiv:1902.09062 (2019).

[114]

Pierre Hansen, Nenad Mladenović, and José A. Moreno Pérez. 2010. Variable neighbourhood search: Methods and applications. Ann. Operat. Res. 175, 1 (2010), 367--407.

[115]

Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil Devanur, Greg Ganger, and Phil Gibbons. 2018. Pipedream: Fast and efficient pipeline parallel dnn training. arXiv preprint arXiv:1806.03377 (2018).

[116]

John A. Hartigan and Manchek A. Wong. 1979. Algorithm AS 136: A k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28, 1 (1979).

[117]

Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross B. Girshick. 2017. Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). 2980--2988.

[118]

Xi He, Dheevatssa Mudigere, Mikhail Smelyanskiy, and Martin Takác. 2017. Distributed hessian-free optimization for deep neural network. In Proceedings of the Workshops at the 31st AAAI Conference on Artificial Intelligence.

[119]

Geoffrey Hinton. 2019. RMSprop. Retrieved from http://www.cs.toronto.edu/ tijmen/csc321/slides/lecture_slides_lec6.pdf.

[120]

Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015).

[121]

Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Greg Ganger, and Eric P Xing. 2013. More effective distributed ml via a stale synchronous parallel parameter server. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’13). 1223--1231.

[122]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735--1780.

Digital Library

[123]

Steven C. H. Hoi, Rong Jin, Jianke Zhu, and Michael R. Lyu. 2006. Batch mode active learning and its application to medical image classification. In Proceedings of the 23rd International Conference on Machine Learning (ICML’06). ACM, 417--424.

[124]

Steven C. H. Hoi, Doyen Sahoo, Jing Lu, and Peilin Zhao. 2018. Online learning: A comprehensive survey. arXiv preprint arXiv:1802.02871 (2018).

[125]

Lu Hou, Ruiliang Zhang, and James T. Kwok. 2019. Analysis of quantized models. In Proceedings of the International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=ryM_IoAqYX.

[126]

Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017).

[127]

Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R. Ganger, Phillip B. Gibbons, and Onur Mutlu. 2017. Gaia: Geo-distributed machine learning approaching LAN speeds. In Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI’17). 629--647.

[128]

Chi-Hung Hsu, Shu-Huan Chang, Jhao-Hong Liang, Hsin-Ping Chou, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, and Da-Cheng Juan. 2018. Monas: Multi-objective neural architecture search using reinforcement learning. arXiv preprint arXiv:1806.10332 (2018).

[129]

Hanqing Hu, Mehmed Kantardzic, and Tegjyot S. Sethi. 2019. No free lunch theorem for concept drift detection in streaming data classification: A review. Wires data Min. Knowl. 10, 2 (2019), e1327.

[130]

Tiansi Hu and Yunsi Fei. 2010. QELAR: A machine-learning-based adaptive routing protocol for energy-efficient and lifetime-extended underwater sensor networks. IEEE Transactions on Mobile Computing 9, 6 (2010), 796–809.

Digital Library

[131]

Gao Huang, Shichen Liu, Laurens Van der Maaten, and Kilian Q. Weinberger. 2018. Condensenet: An efficient densenet using learned group convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 2752--2761.

[132]

Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, Yonghui Wu, et al. 2019. Gpipe: Efficient training of giant neural networks using pipeline parallelism. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’19). 103--112.

[133]

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2017. Quantized neural networks: Training neural networks with low precision weights and activations. J. Mach. Learn. Res. 18, 1 (2017), 6869--6898.

Digital Library

[134]

Zhouyuan Huo, Bin Gu, Qian Yang, and Heng Huang. 2018. Decoupled parallel backpropagation with convergence guarantee. arXiv preprint arXiv:1804.10574 (2018).

[135]

Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015).

Digital Library

[136]

Jozef Ivanecky and Stephan Mehlhase. 2012. An in-car speech recognition system for disabled drivers. In Proceedings of the International Conference on Text, Speech and Dialogue. Springer, 505--512.

[137]

Homayoun Jamshidi, Thomas Lukaszewicz, Amin Kashi, Ansel Berghuvud, Hans-Jurgen Zepernick, and Siamak Khatibi. 2011. Fusion of digital map traffic signs and camera-detected signs. In Proceedings of the International Conference on Signal Processing and Communication Systems (ICSPCS’11). IEEE, 1--7.

[138]

Sylvain Jeaugey. 2017. NCCL 2.0. Retrieved from http://on-demand.gputechconf.com/gtc/2017/presentation/s7155-jeaugey-nccl.pdf.

[139]

Devki Nandan Jha, Peter Michalak, Zhenyu Wen, Paul Watson, and Rajiv Ranjan. 2019. Multi-objective deployment of data analysis operations in heterogeneous IoT infrastructure. IEEE Trans. Industr. Inform. (2019), 1--1.

[140]

Zhihao Jia, Matei Zaharia, and Alex Aiken. 2018. Beyond data and model parallelism for deep neural networks. arXiv preprint arXiv:1807.05358 (2018).

[141]

Peter Jin, Boris Ginsburg, and Kurt Keutzer. 2018. Spatially parallel convolutions. In 6th International Conference on Learning Representations (ICLR'17). https://openreview.net/forum?id=S1Yt0d1vG.

[142]

Ajay J. Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos. 2009. Multi-class active learning for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2372--2379.

[143]

Delphine Jouan-Rimbaud, Desire-Luc Massart, Riccardo Leardi, and Onno E. De Noord. 1995. Genetic algorithms as a tool for wavelength selection in multivariate calibration. Analyt. Chem. 67, 23 (1995), 4295--4301.

[144]

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings et al. 2019. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019).

[145]

Ronald Kemker, Marc McClure, Angelina Abitino, Tyler L. Hayes, and Christopher Kanan. 2018. Measuring catastrophic forgetting in neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence.

[146]

Hanjoo Kim, Minkyu Kim, Dongjoo Seo, Jinwoong Kim, Heungseok Park, Soeun Park, Hyunwoo Jo, KyungHyun Kim, Youngil Yang, Youngkwan Kim, et al. 2018. Nsml: Meet the mlaas platform with a real-world case study. arXiv preprint arXiv:1810.09957 (2018).

[147]

Jin Kyu Kim, Qirong Ho, Seunghak Lee, Xun Zheng, Wei Dai, Garth A. Gibson, and Eric P. Xing. 2016. STRADS: A distributed framework for scheduled model parallel machine learning. In Proceedings of the European Conference on Computer Systems (EuroSys’16). 1--16.

[148]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[149]

Constantinos Kolias, Vasilis Kolias, and Georgios Kambourakis. 2017. TermID: A distributed swarm intelligence-based approach for wireless intrusion detection. International Journal of Information Security 16, 4 (2017), 401–416.

Digital Library

[150]

Vijay R. Konda and John N. Tsitsiklis. 2000. Actor-critic algorithms. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’00). 1008--1014.

[151]

Jakub Konečnỳ, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik. 2016. Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527 (2016).

[152]

Weicong Kong, Zhao Yang Dong, Youwei Jia, David J. Hill, Yan Xu, and Yuan Zhang. 2017. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 10, 1 (2017), 841--851.

[153]

Nupur Kothari, Todd Millstein, and Ramesh Govindan. 2008. Deriving state machines from TinyOS programs using symbolic execution. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN’08). IEEE Computer Society.

Digital Library

[154]

Alex Krizhevsky. 2014. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997 (2014).

[155]

Veljko Krunic, Eric Trumpler, and Richard Han. 2007. NodeMD: Diagnosing node-level faults in remote wireless sensor systems. In Proceedings of the ACM International Conference on Mobile Systems, Applications, and Services (MobiSys’07). ACM, 43--56.

Digital Library

[156]

Pradeep Kumar, Himaanshu Gauba, Partha Pratim Roy, and Debi Prosad Dogra. 2017. Coupled HMM-based multi-sensor data fusion for sign language recognition. Pattern Recogn. Lett. 86 (2017), 1--8.

[157]

Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016. Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016).

[158]

Billy Pik Lik Lau, Sumudu Hasala Marakkalage, Yuren Zhou, Naveed Ul Hassan, Chau Yuen, Meng Zhang, and U-Xuan Tan. 2019. A survey of data fusion in smart city applications. Info. Fusion 52 (2019), 357--374.

[159]

Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan Oseledets, and Victor Lempitsky. 2014. Speeding-up convolutional neural networks using fine-tuned cp-decomposition. arXiv preprint arXiv:1412.6553 (2014).

[160]

Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, and Eric P. Xing. 2014. On model parallelization and scheduling strategies for distributed machine learning. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’14). 2834--2842.

[161]

Hongyang Li, Wanli Ouyang, and Xiaogang Wang. 2016. Multi-bias non-linear activation in deep neural networks. In Proceedings of the International Conference on Machine Learning. 221--229.

[162]

Ji Li, Hui Gao, Tiejun Lv, and Yueming Lu. 2018. Deep reinforcement learning-based computation offloading and resource allocation for MEC. In Proceedings of the Wireless Communications and Networking Conference (WCNC’18). IEEE, 1--6.

[163]

Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky. 2016. Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541 (2016).

[164]

Li Li, Yisheng Lv, and Fei-Yue Wang. 2016. Traffic signal timing via deep reinforcement learning. IEEE/CAA J. Automat. Sinica 3, 3 (2016), 247--254.

[165]

Liping Li, Wei Xu, Tianyi Chen, Georgios B. Giannakis, and Qing Ling. 2019. Rsa: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 1544--1551.

Digital Library

[166]

Mu Li, David G. Andersen, Jun Woo Park, Alexander J. Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J. Shekita, and Bor-Yiing Su. 2014. Scaling distributed machine learning with the parameter server. In Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI’14). 583--598.

Digital Library

[167]

Mu Li, David G. Andersen, Alexander J. Smola, and Kai Yu. 2014. Communication efficient distributed machine learning with the parameter server. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’ (2014).

[168]

Rui Li, Chaoyun Zhang, Paul Patras, Razvan Stanica, and Fabrice Valois. 2019. Learning driven mobility control of airborne base stations in emergency networks. ACM SIGMETRICS Perform. Eval. Rev. 46, 3 (2019), 163–166.

Digital Library

[169]

Jie Lin, Wei Yu, Nan Zhang, Xinyu Yang, Hanlin Zhang, and Wei Zhao. 2017. A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4, 5 (2017), 1125--1142.

[170]

Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, and Martin Jaggi. 2018. Don’t use large mini-batches, use local SGD. arXiv preprint arXiv:1808.07217 (2018).

[171]

Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J. Dally. 2017. Deep gradient compression: Reducing the communication bandwidth for distributed training. arXiv preprint arXiv:1712.01887 (2017).

[172]

Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy. 2018. Progressive neural architecture search. In Proceedings of the European Conference on Computer Vision (ECCV’18). 19--34.

Digital Library

[173]

Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu. 2017. Hierarchical representations for efficient architecture search. arXiv preprint arXiv:1711.00436 (2017).

[174]

Li Liu, Miao Zhang, Yuqing Lin, and Liangjuan Qin. 2014. A survey on workflow management and scheduling in cloud computing. In Proceedings of the IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid’14). IEEE, 837--846.

Digital Library

[175]

Manuel Lopez-Martin, Belen Carro, Antonio Sanchez-Esguevillas, and Jaime Lloret. 1967. Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot. Sensors 17, 9 (1967).

[176]

Qianxi Lu, Tao Peng, Wei Wang, Wenbo Wang, and Chao Hu. 2010. Utility-based resource allocation in uplink of OFDMA-based cognitive radio networks. Int. J. Commun. Syst. 23, 2 (2010), 252--274.

Digital Library

[177]

Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’15) (2015).

[178]

Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun. 2018. Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European Conference on Computer Vision (ECCV’18).

Digital Library

[179]

David J. C. MacKay and David J. C. MacKay. 2003. Information Theory, Inference and Learning Algorithms. Cambridge University Press.

[180]

Ritesh Madan, Jaber Borran, Ashwin Sampath, Naga Bhushan, Aamod Khandekar, and Tingfang Ji. 2010. Cell association and interference coordination in heterogeneous LTE-A cellular networks. IEEE Journal on Selected Areas in Communications 28, 9 (2010), 1479--1489.

Digital Library

[181]

Ritesh Madan, Stephen P. Boyd, and Sanjay Lall. 2010. Fast algorithms for resource allocation in wireless cellular networks. IEEE/ACM Trans. Netw. 18, 3 (2010), 973--984.

Digital Library

[182]

Hongzi Mao, Mohammad Alizadeh, Ishai Menache, and Srikanth Kandula. 2016. Resource management with deep reinforcement learning. In Proceedings of the 15th ACM Workshop on Hot Topics in Networks. ACM.

Digital Library

[183]

Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. 2017. Neural adaptive video streaming with pensieve. In Proceedings of the ACM Special Interest Group on Data Communications (SIGCOMM’17). ACM.

Digital Library

[184]

A Marcano-Cedeno et al. 2010. Feature selection using sequential forward selection and classification applying artificial metaplasticity neural network. In Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON’10). IEEE.

[185]

Michael Mathieu, Mikael Henaff, and Yann LeCun. 2013. Fast training of convolutional networks through ffts. arXiv preprint arXiv:1312.5851 (2013).

[186]

W. F. McColl. 1995. Bulk synchronous parallel computing. Abstract Machine Models for Highly Parallel Computers, Oxford University Press, Oxford (1995).

[187]

Ryan McDonald, Keith Hall, and Gideon Mann. 2010. Distributed training strategies for the structured perceptron. In Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’10). Association for Computational Linguistics, 456--464.

[188]

H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al. 2016. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016).

[189]

H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2017. Learning differentially private recurrent language models. Proceedings of the International Conference on Learning Representations (ICLR’18).

[190]

Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, et al. 2016. Mllib: Machine learning in apache spark. J. Mach. Learn. Res. 17, 1 (2016), 1235--1241.

Digital Library

[191]

Andreas Merentitis and Christian Debes. 2015. Automatic fusion and classification using random forests and features extracted with deep learning. In Proceedings of the IEEE Geoscience and Remote Sensing Society (IGARSS’15). IEEE, 2943--2946.

[192]

E. M. El Mhamdi and R. Guerraoui. 2017. When neurons fail. In Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS’17). 1028--1037.

[193]

Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, and Jeff Dean. 2018. A hierarchical model for device placement. In International Conference on Learning Representations. https://openreview.net/forum?id=Hkc-TeZ0W.

[194]

Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, and Jeff Dean. 2017. Device placement optimization with reinforcement learning. In Proceedings of the International Conference of Machine Learning (ICML’17). JMLR.org, 2430--2439.

[195]

Tom M. Mitchell. 1999. Machine learning and data mining. Commun. ACM 42, 11 (1999), 30--36.

Digital Library

[196]

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML’16). 1928--1937.

[197]

Mehdi Mohammadi, Ala Al-Fuqaha, Sameh Sorour, and Mohsen Guizani. 2018. Deep learning for IoT big data and streaming analytics: A survey. IEEE Commun. Surveys Tutor. 20, 4 (2018).

Digital Library

[198]

Philipp Moritz, Robert Nishihara, and Michael Jordan. 2016. A linearly convergent stochastic L-BFGS algorithm. In Proceedings of the Conference on Artificial Intelligence and Statistics. 249--258.

[199]

Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, et al. 2018. Ray: A distributed framework for emerging AI applications. In Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI’18). 561--577.

[200]

Md Shirajum Munir, Sarder Fakhrul Abedin, Md Golam Rabiul Alam, Do Hyeon Kim, and Choong Seon Hong. 2017. RNN-based energy demand prediction for smart-home in smart-grid framework. Journal of Korean Information Science Society (2017), 437--439.

[201]

Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, and Gavin Taylor. 2019. Autonomous management of energy-harvesting IoT nodes using deep reinforcement learning. arXiv:1905.04181 (2019).

[202]

Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever. 2019. Deep double descent: Where bigger models and more data hurt. arXiv preprint arXiv:1912.02292 (2019).

[203]

P. Naraei, A. Abhari, and A. Sadeghian. 2016. Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data. In Proceedings of the Future Technologies Conference (FTC’16). 848--852.

[204]

Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee, Satish Rao, and J. D. Tygar. 2012. Query strategies for evading convex-inducing classifiers. J. Mach. Learn. Res. 13, May (2012), 1293--1332.

[205]

Dong Oh and Il Yun. 2018. Residual error-based anomaly detection using auto-encoder in SMD machine sound. Sensors 18, 5 (2018), 1308.

[206]

Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, and Satoshi Matsuoka. 2018. Second-order optimization method for large mini-batch: Training resnet-50 on imagenet in 35 epochs. arXiv preprint:1811.12019 (2018).

[207]

Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, and Christopher Re. 2016. Cyclades: Conflict-free asynchronous machine learning. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’16). 2568--2576.

[208]

Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami. 2017. Practical black-box attacks against machine learning. In ASIACCS 2017. ACM, 506--519.

Digital Library

[209]

Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami. 2016. The limitations of deep learning in adversarial settings. In Proceedings of the IEEE European Symposium on Security and Privacy (EuroS8P’16). IEEE.

[210]

German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter. 2019. Continual lifelong learning with neural networks: A review. Neural Netw. 113 (2019), 54--71.

Digital Library

[211]

Ronald Parr and Stuart J. Russell. 1998. Reinforcement learning with hierarchies of machines. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’98).

[212]

Zhenghao Peng, Xuyang Chen, Chengwen Xu, Naifeng Jing, Xiaoyao Liang, Cewu Lu, and Li Jiang. 2018. AXNet: ApproXimate computing using an end-to-end trainable neural network. In Proceedings of the International Conference on Computer-Aided Design (ICCAD’18). ACM, 11.

Digital Library

[213]

Alain Petrowski, Gerard Dreyfus, and Claude Girault. 1993. Performance analysis of a pipelined backpropagation parallel algorithm. IEEE Trans. Neural Netw. 4, 6 (1993), 970--981.

Digital Library

[214]

Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean. 2018. Efficient neural architecture search via parameter sharing. arXiv preprint arXiv:1802.03268 (2018).

[215]

Joshua Plasse and Niall Adams. 2016. Handling delayed labels in temporally evolving data streams. In Proceedings of the IEEE International Conference on Big Data (BigData’16). IEEE, 2416--2424.

[216]

Daniel Povey, Xioahui Zhang, and Sanjeev Khudanpur. 2017. Parallel training of DNNs with Natural Gradient and Parameter Averaging. Proceedings of the International Conference on Learning Representations (ICLR’19) 2015.

[217]

Peter W. Protzel, Daniel L. Palumbo, and Michael K. Arras. 1993. Performance and fault-tolerance of neural networks for optimization. IEEE Trans. Neural Netw. 4, 4 (1993), 600--614.

Digital Library

[218]

Ning Qian. 1999. On the momentum term in gradient descent learning algorithms. Neural Netw. 12, 1 (1999), 145--151.

Digital Library

[219]

Aurick Qiao, Bryon Aragam, Bingjing Zhang, and Eric P. Xing. 2018. Fault tolerance in iterative-convergent machine learning. arXiv preprint arXiv:1810.07354 (2018).

[220]

John Quackenbush. 2002. Microarray data normalization and transformation. Nature Genet. 32, 4s (2002), 496.

[221]

Deirdre Quillen, Eric Jang, Ofir Nachum, Chelsea Finn, Julian Ibarz, and Sergey Levine. 2018. Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods. In Proceedings of the International Conference on Robotics and Automation (ICRA’18).

[222]

J. Ross Quinlan. 1986. Induction of decision trees. Mach. Learn. 1, 1 (1986), 81--106.

[223]

J. Ross Quinlan. 2014. C4. 5: Programs for Machine Learning. Elsevier.

Digital Library

[224]

Rajat Raina, Anand Madhavan, and Andrew Y. Ng. 2009. Large-scale deep unsupervised learning using graphics processors. In Proceedings of the 26th Annual International Conference on machime Learning (ICML’09). ACM.

[225]

Nipun Ramakrishnan and Tarun Soni. 2018. Network Traffic Prediction Using Recurrent Neural Networks. In Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA’18). IEEE.

[226]

Brian Randell. 1975. System structure for software fault tolerance. IEEE Trans. Softw. Eng. 2 (1975), 220--232.

Digital Library

[227]

Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. 2016. Xnor-net: Imagenet classification using binary convolutional neural networks. In Proceedings of the European Conference on Computer Vision. Springer.

[228]

Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le. 2019. Regularized evolution for image classifier architecture search. In Proceedings of the AAAI Conference on Artificial Intelligence.

[229]

Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu. 2011. Hogwild: A lock-free approach to parallelizing stochastic gradient descent. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’11). 693--701.

[230]

Liangliang Ren, Xin Yuan, Jiwen Lu, Ming Yang, and Jie Zhou. 2018. Deep reinforcement learning with iterative shift for visual tracking. In Proceedings of the European Conference on Computer Vision (ECCV’18). 684--700.

[231]

Konrad Rieck, Philipp Trinius, Carsten Willems, and Thorsten Holz. 2011. Automatic analysis of malware behavior using machine learning. J. Comput. Secur. 19, 4 (2011), 639--668.

[232]

Henriette Röger and Ruben Mayer. 2019. A comprehensive survey on parallelization and elasticity in stream processing. ACM Comput. Surveys 52, 2 (2019), 36.

Digital Library

[233]

Rodrigo Roman, Jianying Zhou, and Javier Lopez. 2013. On the features and challenges of security and privacy in distributed internet of things. Comput. Netw. 57, 10 (2013), 2266--2279.

Digital Library

[234]

Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. 2014. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014).

[235]

Frederik Ruelens, Bert J. Claessens, Stijn Vandael, Bart De Schutter, Robert Babuška, and Ronnie Belmans. 2016. Residential demand response of thermostatically controlled loads using batch reinforcement learning. IEEE Trans. Smart Grid 8, 5 (2016), 2149--2159.

[236]

Vahideh Saeidi, Biswajeet Pradhan, Mohammed O. Idrees, and Zulkiflee Abd Latif. 2014. Fusion of airborne lidar with multispectral spot 5 image for enhancement of feature extraction using Dempster–Shafer theory. IEEE Transactions on Geoscience and Remote Sensing 52, 10 (2014), 6017--6025.

[237]

Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18).

[238]

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017).

[239]

Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu. 2014. 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns. In Proceedings of the Annual Conference of the International Speech Communication (INTERSPEECH’14).

[240]

Alexander Sergeev and Mike Del Balso. 2018. Horovod: Fast and easy distributed deep learning in TensorFlow. arXiv preprint arXiv:1802.05799 (2018).

[241]

Burr Settles. 2012. Active Learning. Synth. Lect. Artific. Intell. Mach. Learn. 6, 1 (2012), 1--114.

[242]

Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein. 2018. Poison frogs! Targeted clean-label poisoning attacks on neural networks. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’18).

[243]

Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee. 2016. Understanding and improving convolutional neural networks via concatenated rectified linear units. In Proceedings of the International Conference on Machine Learning (ICML’16). 2217--2225.

[244]

Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, and Blake Hechtman. 2018. Mesh-TensorFlow: Deep learning for supercomputers. In Proceedings of the International Conference on Neural Information Processing Systems.

[245]

Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. 2016. Edge computing: Vision and challenges. IEEE Internet Things J. 3, 5 (2016), 637--646.

[246]

Jonathon Shlens. 2014. A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100 (2014).

[247]

Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017. Membership inference attacks against machine learning models. In Proceedings of the IEEE Symposium on Security and Privacy (SP’17). IEEE, 3--18.

[248]

Alexander Shustanov and Pavel Yakimov. 2017. CNN design for real-time traffic sign recognition. Procedia Eng. 201 (2017), 718--725.

[249]

Sabrina Sicari, Alessandra Rizzardi, Luigi Alfredo Grieco, and Alberto Coen-Porisini. 2015. Security, privacy and trust in Internet of Things: The road ahead. Comput. Netw. 76 (2015), 146--164.

Digital Library

[250]

Laurent Sifre and Stéphane Mallat. 2014. Rigid-motion scattering for image classification. Ph. D. Dissertation. Ecole Normale Superieure, Paris, France.

[251]

Amit Kumar Sikder, Hidayet Aksu, and A. Selcuk Uluagac. 2017. 6thsense: A context-aware sensor-based attack detector for smart devices. In Proceedings of the 26th USENIX Security Symposium. 397--414.

[252]

Kiran Jot Singh and Divneet Singh Kapoor. 2017. Create your own Internet of Things: A survey of IoT platforms. IEEE Consum. Electron. Mag. 6, 2 (2017), 57--68.

[253]

Sukhpal Singh and Inderveer Chana. 2016. A survey on resource scheduling in cloud computing: Issues and challenges. J. Grid Comput. 14, 2 (2016), 217--264.

Digital Library

[254]

Satinder P. Singh. 1992. Reinforcement learning with a hierarchy of abstract models. In Proceedings of the National Conference on Artificial Intelligence.

Digital Library

[255]

Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, and Quoc V. Le. 2017. Don’t decay the learning rate, increase the batch size. arXiv preprint arXiv:1711.00489 (2017).

[256]

Evan R. Sparks, Ameet Talwalkar, Daniel Haas, Michael J. Franklin, Michael I. Jordan, and Tim Kraska. 2015. Automating model search for large scale machine learning. In Proceedings of the 6th ACM Symposium on Cloud Computing. ACM.

Digital Library

[257]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1 (2014), 1929--1958.

Digital Library

[258]

Jacob Steinhardt, Pang Wei W. Koh, and Percy S. Liang. 2017. Certified defenses for data poisoning attacks. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’ (2017). 3517--3529.

[259]

Sebastian Urban Stich. 2019. Local SGD converges fast and communicates little. In Proceedings of the International Conference on Learning Representations (ICLR’19).

[260]

Ion Stoica, Dawn Song, Raluca Ada Popa, David Patterson, Michael W. Mahoney, Randy Katz, Anthony D. Joseph, Michael Jordan, Joseph M. Hellerstein, Joseph E. Gonzalez, et al. 2017. A Berkeley view of systems challenges for ai. arXiv preprint arXiv:1712.05855 (2017).

[261]

Nikko Strom. 2015. Scalable distributed DNN training using commodity GPU cloud computing. In Proceedings of the Annual Conference of the International Speech Communication (INTERSPEECH’15).

[262]

Octavian Suciu, Radu Marginean, Yigitcan Kaya, Hal Daume III, and Tudor Dumitras. 2018. When does machine learning FAIL? Generalized transferability for evasion and poisoning attacks. In Proceedings of the USENIX Security Symposium (USENIXSecurity’18). 1299--1316.

[263]

Salmin Sultana, Daniele Midi, and Elisa Bertino. 2014. Kinesis: A security incident response and prevention system for wireless sensor networks. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’14). ACM.

Digital Library

[264]

James Supancic III and Deva Ramanan. 2017. Tracking as online decision-making: Learning a policy from streaming videos with reinforcement learning. In Proceedings of the International Conference on Computer Vision (ICCV’17). 322--331.

[265]

Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. MIT Press.

Digital Library

[266]

Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu. 2018. A survey on deep transfer learning. In Proceedings of the International Conference on Artificial Neural Networks. Springer, 270--279.

[267]

Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V. Le. 2019. Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19).

[268]

Hao Tang, Wei Wang, Songsong Wu, Xinya Chen, Dan Xu, Nicu Sebe, and Yan Yan. 2019. Expression conditional gan for facial expression-to-expression translation. In Proceedings of the International Conference on Image Processing (ICIP’19). IEEE, 4449--4453.

[269]

TensorFlow. 2019. High performance inference with TensorRT Integration. Retrieved from https://medium.com/tensorflow/high-performance-inference-with-tensorrt-integration-c4d78795fbfe.

[270]

Sebastian Thrun and Anton Schwartz. 1995. Finding structure in reinforcement learning. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’95).

[271]

James T. Townsend. 1971. Theoretical analysis of an alphabetic confusion matrix. Percept. Psychophys. 9, 1 (1971), 40--50.

[272]

Florian Tramer, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart. 2016. Stealing machine learning models via prediction apis. In Proceedings of the 25th USENIX Security Symposium (USENIXSecurity’16). 601--618.

Digital Library

[273]

Amin Ullah, Jamil Ahmad, Khan Muhammad, Muhammad Sajjad, and Sung Wook Baik. 2018. Action recognition in video sequences using deep bi-directional LSTM With CNN features. IEEE Access 6 (2018), 1155--1166.

[274]

Michel Vacher, Benjamin Lecouteux, Javier Serrano Romero, Moez Ajili, Francois Portet, and Solange Rossato. 2015. Speech and speaker recognition for home automation: Preliminary results. In Proceedings of the International Conference on Speech Technology and Human-Computer Dialogue (SpeD’15). IEEE.

[275]

Leslie G. Valiant. 1984. A theory of the learnable. In Proceedings of the 16th Annual ACM Symposium on Theory of Computing. ACM, 436--445.

Digital Library

[276]

Vincent Vanhoucke, Andrew Senior, and Mark Z. Mao. 2011. Improving the speed of neural networks on CPUs. In Proceedings of the Deep Learning and Unsupervised Feature Learning Workshop (NIPS'11).

[277]

Joaquin Vanschoren. 2018. Meta-learning: A survey. arXiv preprint arXiv:1810.03548 (2018).

[278]

Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, and Kate Saenko. 2014. Translating videos to natural language using deep recurrent neural networks. arXiv preprint arXiv:1412.4729 (2014).

[279]

Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, and Silvio Savarese. 2018. Generalizing to unseen domains via adversarial data augmentation. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’18). 5334--5344.

[280]

Gang Wang, Jinxing Hao, Jian Ma, and Lihua Huang. 2010. A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 37, 9 (2010), 6225--6232.

Digital Library

[281]

Jason Wang and Luis Perez. 2017. The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621.

[282]

Meng Wang and Xian-Sheng Hua. 2011. Active learning in multimedia annotation and retrieval: A survey. ACM Trans. Intell. Syst. Technol. 2, 2 (2011), 1--21.

Digital Library

[283]

Meng Wang, Xian Sheng Hua, Tao Mei, Jinhui Tang, Guo Jun Qi, Yan Song, and Li Rong Dai. 2007. Interactive video annotation by multi concept multi modality active learning. In Proceedings of the IEEE International Conference on Semantic Computing (ICSC’07).

Digital Library

[284]

Minjie Wang, Chien-chin Huang, and Jinyang Li. 2019. Supporting very large models using automatic dataflow graph partitioning. In Proceedings of the European Conference on Computer Systems (EuroSys’19). 1--17.

Digital Library

[285]

Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, and Kevin Chan. 2018. When edge meets learning: Adaptive control for resource-constrained distributed machine learning. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’18). IEEE, 63--71.

[286]

Denis Weerasiri, Moshe Chai Barukh, Boualem Benatallah, Quan Z. Sheng, and Rajiv Ranjan. 2017. A taxonomy and survey of cloud resource orchestration techniques. ACM Comput. Surveys 50, 2 (2017), 26.

Digital Library

[287]

Jinliang Wei, Wei Dai, Aurick Qiao, Qirong Ho, Henggang Cui, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, and Eric P. Xing. 2015. Managed communication and consistency for fast data-parallel iterative analytics. In Proceedings of the ACM Symposium on Cloud Computing (SoCC’15). ACM, 381--394.

[288]

Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. 2017. Terngrad: Ternary gradients to reduce communication in distributed deep learning. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’17). 1509--1519.

[289]

Zhenyu Wen, Pramod Bhatotia, Ruichuan Chen, Myungjin Lee, et al. 2018. Approxiot: Approximate analytics for edge computing. In Proceedings of the IEEE International Conference on Distributed Computing Systems (ICDCS’18). IEEE.

[290]

Zheng Wen, Daniel O’Neill, and Hamid Maei. 2015. Optimal demand response using device-based reinforcement learning. IEEE Trans. Smart Grid 6, 5 (2015), 2312--2324.

[291]

Zhenyu Wen, Renyu Yang, Peter Garraghan, Tao Lin, Jie Xu, and Michael Rovatsos. 2017. Fog orchestration for internet of things services. IEEE Internet Comput. 21, 2 (2017), 16--24.

Digital Library

[292]

Shuang Wu, Guoqi Li, Feng Chen, and Luping Shi. 2018. Training and inference with integers in deep neural networks. In International Conference on Learning Representations.

[293]

Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint:1609.08144 (2016).

[294]

Han Xiao, Huang Xiao, and Claudia Eckert. 2012. Adversarial label flips attack on support vector machines. In Proceedings of the European Conference on Artificial Intelligence (ECAI’12). 870--875.

[295]

Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, and Yaoliang Yu. 2015. Petuum: A new platform for distributed machine learning on big data. IEEE Trans. Big Data 1, 2 (2015), 49--67.

[296]

Caiming Xiong, Victor Zhong, and Richard Socher. 2017. Dcn+: Mixed objective and deep residual coattention for question answering. arXiv preprint arXiv:1711.00106 (2017).

[297]

Xiaobin Xu, Jin Zheng, Jian-bo Yang, Dong-ling Xu, and Yu-wang Chen. 2017. Data classification using evidence reasoning rule. Knowl.-Based Syst. 116 (2017), 144--151.

Digital Library

[298]

Zhiyuan Xu, Yanzhi Wang, Jian Tang, Jing Wang, and Mustafa Cenk Gursoy. 2017. A deep reinforcement learning-based framework for power-efficient resource allocation in cloud RANs. In Proceedings of the IEEE International Conference on Communications (ICC’17). IEEE.

[299]

Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, and Randy Katz. 2016. Multi-task learning for straggler avoiding predictive job scheduling. J. Mach. Learn. Res. 17, 1 (2016), 3692--3728.

Digital Library

[300]

Chaoqun Yang, Li Feng, Heng Zhang, Shibo He, and Zhiguo Shi. 2018. A novel data fusion algorithm to combat false data injection attacks in networked radar systems. IEEE Trans. Signal Info. Process. Netw. 4, 1 (2018), 125--136.

[301]

Emre Yigitoglu, Mohamed Mohamed, Ling Liu, and Heiko Ludwig. 2017. Foggy: A framework for continuous automated IoT application deployment in fog computing. In Proceedings of the Administrative Information Management Services (AIMS’17). IEEE, 38--45.

[302]

Yang You, Igor Gitman, and Boris Ginsburg. 2017. Large batch training of convolutional networks. arXiv preprint arXiv:1708.03888 (2017).

[303]

Hao Yu, Sen Yang, and Shenghuo Zhu. 2018. Parallel restarted SGD for non-convex optimization with faster convergence and less communication. arXiv preprint:1807.06629 2, 4 (2018), 7.

[304]

Hao Yu, Sen Yang, and Shenghuo Zhu. 2019. Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning. In Proceedings of the AAAI Conference on Artificial Intelligence.

[305]

Sangdoo Yun, Jongwon Choi, Youngjoon Yoo, Kimin Yun, and Jin Young Choi. 2017. Action-decision networks for visual tracking with deep reinforcement learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 2711--2720.

[306]

Sergey Zagoruyko et al. 2016. Paying more attention to attention: Improving performance of convolutional neural networks via attention transfer. arXiv:1612.03928 (2016).

[307]

Shuangfei Zhai, Yu Cheng, Zhongfei Mark Zhang, and Weining Lu. 2016. Doubly convolutional neural networks. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’ (2016). 1082--1090.

[308]

Zhi-Hui Zhan, Xiao-Fang Liu, Yue-Jiao Gong, Jun Zhang, Henry Shu-Hung Chung, and Yun Li. 2015. Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surveys 47, 4 (2015), 63.

Digital Library

[309]

Ce Zhang, Arun Kumar, and Christopher Ré. 2016. Materialization optimizations for feature selection workloads. ACM Trans. Database Syst. 41, 1 (2016), 2.

Digital Library

[310]

Chaoyun Zhang, Paul Patras, and Hamed Haddadi. 2019. Deep learning in mobile and wireless networking: A survey. IEEE Commun. Surveys Tutor. (2019).

[311]

Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, and Ce Zhang. 2017. Zipml: Training linear models with end-to-end low precision, and a little bit of deep learning. In Proceedings of the International Conference on Machine Learning (ICML’17). JMLR.org, 4035--4043.

[312]

Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, and Christopher Ré. 2016. Parallel SGD: When does averaging help? arXiv preprint arXiv:1606.07365 (2016).

[313]

Quan-shi Zhang and Song-Chun Zhu. 2018. Visual interpretability for deep learning: A survey. Front. Info. Technol. Electron. Eng. 19, 1 (2018), 27--39.

[314]

Sixin Zhang, Anna E Choromanska, and Yann LeCun. 2015. Deep learning with elastic averaging SGD. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’15). 685--693.

[315]

Weishan Zhang, Wuwu Guo, Xin Liu, Yan Liu, Jiehan Zhou, Bo Li, Qinghua Lu, and Su Yang. 2018. LSTM-based analysis of industrial IoT equipment. IEEE Access 6 (2018), 23551--23560.

[316]

Wen-An Zhang, Bo Chen, and Michael Z. Q. Chen. 2016. Hierarchical fusion estimation for clustered asynchronous sensor networks. IEEE Trans. Automat. Control 61, 10 (2016), 3064--3069.

[317]

Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. 2018. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’18). 6848--6856.

[318]

Caidan Zhao, Caiyun Chen, Zeping He, and Zhiqiang Wu. 2018. Application of auxiliary classifier wasserstein generative adversarial networks in wireless signal classification of illegal unmanned aerial vehicles. Appl. Sci. 8, 12 (2018), 2664.

[319]

Caidan Zhao, Mingxian Shi, Zhibiao Cai, and Caiyun Chen. 2018. Research on the open-categorical classification of the Internet-of-things based on generative adversarial networks. Appl. Sci. 8, 12 (2018), 2351.

[320]

Dongbin Zhao, Yaran Chen, and Le Lv. 2016. Deep reinforcement learning with visual attention for vehicle classification. IEEE Trans. Cogn. Dev. Syst. 9, 4 (2016), 356--367.

[321]

Binbin Zhou, Jiannong Cao, Xiaoqin Zeng, and Hejun Wu. 2010. Adaptive traffic light control in wireless sensor network-based intelligent transportation system. In Proceedings of the IEEE Vehicular Technology Conference (VTC’10). IEEE, 1--5.

[322]

Shusen Zhou, Qingcai Chen, and Xiaolong Wang. 2013. Active deep learning method for semi-supervised sentiment classification. Neurocomputing 120 (2013), 536--546.

[323]

Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou. 2016. Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016).

[324]

Yanqi Zhou, Siavash Ebrahimi, Sercan Ö. Arık, Haonan Yu, Hairong Liu, and Greg Diamos. 2018. Resource-efficient neural architect. arXiv preprint arXiv:1806.07912 (2018).

[325]

Martin Zinkevich, Markus Weimer, Lihong Li, and Alex J. Smola. 2010. Parallelized stochastic gradient descent. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’10). 2595--2603.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 4

July 2021

831 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3410467

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Published: 03 August 2020

Online AM: 07 May 2020

Accepted: 01 May 2020

Revised: 01 March 2020

Received: 01 October 2019

Published in CSUR Volume 53, Issue 4

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