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Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence.
P. Blasco and D. Gündüz, “Learning-based optimization of cache content in a small cell base station,” in 2014 IEEE International Conference on Communications (ICC) . IEEE, 2014, pp. 1897–1903
1903
Earlier work this paper cites.
S. M. Johnson, “Optimal two-and three-stage production schedules with setup times included,” Naval research logistics quarterly , vol. 1, no. 1, pp. 61–68, 1954
1954
Earlier work this paper cites.
R. Morris, “Tapered floating point: A new floating-point representation,” IEEE Transactions on Computers , vol. 100, no. 12, pp. 1578–1579, 1971
1971
Earlier work this paper cites.
J. Van Leeuwen, “On the construction of huffman trees.” in ICALP , 1976, pp. 382–410
1976
Earlier work this paper cites.
Y. LeCun, J. S. Denker, and S. A. Solla, “Optimal brain damage,” in Advances in neural information processing systems , 1990, pp. 598–605
1990
Earlier work this paper cites.
B. Hassibi and D. G. Stork, “Second order derivatives for network pruning: Optimal brain surgeon,” in Advances in neural information processing systems , 1993, pp. 164–171
1993
Earlier work this paper cites.
C. Roadknight, I. Marshall, and D. Vearer, “File popularity characterisation,” ACM Sigmetrics Performance Evaluation Review , vol. 27, no. 4, pp. 45–50, 2000
2000
Earlier work this paper cites.
S. Liu, Q. Wang, and G. Liu, “A versatile method of discrete convolution and fft (dc-fft) for contact analyses,” Wear , vol. 243, no. 1-2, pp. 101–111, 2000
2000
Earlier work this paper cites.
J. Hoisko, “Context triggered visual episodic memory prosthesis,” in Digest of Papers. Fourth International Symposium on Wearable Computers . IEEE, 2000, pp. 185–186
2000
Earlier work this paper cites.
M. Ware, E. Frank, G. Holmes, M. Hall, and I. H. Witten, “Interactive machine learning: letting users build classifiers,” International Journal of Human-Computer Studies , vol. 55, no. 3, pp. 281–292, 2001
2001
Earlier work this paper cites.
J. R. Douceur, “The sybil attack,” in International workshop on peer-to-peer systems . Springer, 2002, pp. 251–260
2002
Earlier work this paper cites.
R. Vilalta and Y. Drissi, “A perspective view and survey of meta-learning,” Artificial intelligence review , vol. 18, no. 2, pp. 77–95, 2002
2002
Earlier work this paper cites.
2002
Earlier work this paper cites.
Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, “A neural probabilistic language model,” Journal of machine learning research , vol. 3, no. Feb, pp. 1137–1155, 2003
2003
Earlier work this paper cites.
S. Malki and L. Spaanenburg, “Cnn image processing on a xilinx virtex-ii 6000,” in Proceedings ECCTD , vol. 3, 2003, pp. 261–264
2003
Earlier work this paper cites.
C. Buciluǎ, R. Caruana, and A. Niculescu-Mizil, “Model compression,” in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2006, pp. 535–541
2006
Earlier work this paper cites.
J. B. Predd, S. B. Kulkarni, and H. V. Poor, “Distributed learning in wireless sensor networks,” IEEE Signal Processing Magazine , vol. 23, no. 4, pp. 56–69, 2006
2006
Earlier work this paper cites.
C. Buciluǎ, R. Caruana, and A. Niculescu-Mizil, “Model compression,” in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2006, pp. 535–541
2006
Earlier work this paper cites.
S. Hodges, L. Williams, E. Berry, S. Izadi, J. Srinivasan, A. Butler, G. Smyth, N. Kapur, and K. Wood, “Sensecam: A retrospective memory aid,” in International Conference on Ubiquitous Computing . Springer, 2006, pp. 177–193
2006
Earlier work this paper cites.
Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li, “Multi-probe lsh: efficient indexing for high-dimensional similarity search,” in Proceedings of the 33rd international conference on Very large data bases . VLDB Endowment, 2007, pp. 950–961
2007
Earlier work this paper cites.
M. Schwabacher and K. Goebel, “A survey of artificial intelligence for prognostics.” in AAAI Fall Symposium: Artificial Intelligence for Prognostics , 2007, pp. 108–115
2007
Earlier work this paper cites.
W. T. Ang, P. K. Khosla, and C. N. Riviere, “Nonlinear regression model of a low- g g mems accelerometer,” IEEE Sensors Journal , vol. 7, no. 1, pp. 81–88, 2007
2007
Earlier work this paper cites.
R. G. Baraniuk, “Compressive sensing,” IEEE signal processing magazine , vol. 24, no. 4, 2007
2007
Earlier work this paper cites.
R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proceedings of the 25th international conference on Machine learning . ACM, 2008, pp. 160–167
2008
Earlier work this paper cites.
R. Crane and D. Sornette, “Robust dynamic classes revealed by measuring the response function of a social system,” Proceedings of the National Academy of Sciences , vol. 105, no. 41, pp. 15 649–15 653, 2008
2008
Earlier work this paper cites.
E. Adar, J. Teevan, and S. T. Dumais, “Large scale analysis of web revisitation patterns,” in Proceedings of the SIGCHI conference on Human Factors in Computing Systems . ACM, 2008, pp. 1197–1206
2008
Earlier work this paper cites.
C. Gentry et al. , “Fully homomorphic encryption using ideal lattices.” in Stoc , vol. 9, no. 2009, 2009, pp. 169–178
2009
Earlier work this paper cites.
M. Newman, Networks: an introduction . Oxford university press, 2010
2010
Earlier work this paper cites.
A. He, K. K. Bae, T. R. Newman, J. Gaeddert, K. Kim, R. Menon, L. Morales-Tirado, Y. Zhao, J. H. Reed, W. H. Tranter et al. , “A survey of artificial intelligence for cognitive radios,” IEEE Transactions on Vehicular Technology , vol. 59, no. 4, pp. 1578–1592, 2010
2010
Earlier work this paper cites.
A. Bahrammirzaee, “A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems,” Neural Computing and Applications , vol. 19, no. 8, pp. 1165–1195, 2010
2010
Earlier work this paper cites.
H. Dahrouj and W. Yu, “Coordinated beamforming for the multicell multi-antenna wireless system,” IEEE transactions on wireless communications , vol. 9, no. 5, pp. 1748–1759, 2010
2010
Earlier work this paper cites.
J. G. Andrews, F. Baccelli, and R. K. Ganti, “A tractable approach to coverage and rate in cellular networks,” IEEE Transactions on communications , vol. 59, no. 11, pp. 3122–3134, 2011
2011
Earlier work this paper cites.
C. Dwork, “Differential privacy,” Encyclopedia of Cryptography and Security , pp. 338–340, 2011
2011
Earlier work this paper cites.
J. Yang, S. Sidhom, G. Chandrasekaran, T. Vu, H. Liu, N. Cecan, Y. Chen, M. Gruteser, and R. P. Martin, “Detecting driver phone use leveraging car speakers,” in Proceedings of the 17th annual international conference on Mobile computing and networking . ACM, 2011, pp. 97–108
2011
Earlier work this paper cites.
V. Vanhoucke, A. Senior, and M. Z. Mao, “Improving the speed of neural networks on cpus,” 2011
2011
Earlier work this paper cites.
M.-R. Ra, A. Sheth, L. Mummert, P. Pillai, D. Wetherall, and R. Govindan, “Odessa: enabling interactive perception applications on mobile devices,” in Proceedings of the 9th international conference on Mobile systems, applications, and services . ACM, 2011, pp. 43–56
2011
Earlier work this paper cites.
K. Su, J. Li, and H. Fu, “Smart city and the applications,” in 2011 international conference on electronics, communications and control (ICECC) . IEEE, 2011, pp. 1028–1031
2011
Earlier work this paper cites.
J. L. Hennessy and D. A. Patterson, Computer architecture: a quantitative approach . Elsevier, 2011
2011
Earlier work this paper cites.
P. Marsch and G. P. Fettweis, Coordinated Multi-Point in Mobile Communications: from theory to practice . Cambridge University Press, 2011
2011
Earlier work this paper cites.
M. Rabbi, S. Ali, T. Choudhury, and E. Berke, “Passive and in-situ assessment of mental and physical well-being using mobile sensors,” in Proceedings of the 13th international conference on Ubiquitous computing . ACM, 2011, pp. 385–394
2011
Earlier work this paper cites.
F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the internet of things,” in Proceedings of the first edition of the MCC workshop on Mobile cloud computing , 2012, pp. 13–16
2012
Earlier work this paper cites.
R. Huitl, G. Schroth, S. Hilsenbeck, F. Schweiger, and E. Steinbach, “Tumindoor: An extensive image and point cloud dataset for visual indoor localization and mapping,” in 2012 19th IEEE International Conference on Image Processing . IEEE, 2012, pp. 1773–1776
2012
Earlier work this paper cites.
N. Golrezaei, A. G. Dimakis, and A. F. Molisch, “Wireless device-to-device communications with distributed caching,” in 2012 IEEE International Symposium on Information Theory Proceedings . IEEE, 2012, pp. 2781–2785
2012
Earlier work this paper cites.
B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” in Proceedings of the 29th International Coference on International Conference on Machine Learning , ser. ICML’12. Madison, WI, USA: Omnipress, 2012, p. 1467–1474
2012
Earlier work this paper cites.
H. Guihot, “Renderscript,” in Pro Android Apps Performance Optimization . Springer, 2012, pp. 231–263
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
S. Kosta, A. Aucinas, P. Hui, R. Mortier, and X. Zhang, “Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading,” in 2012 proceedings IEEE Infocom . IEEE, 2012, pp. 945–953
2012
Earlier work this paper cites.
S. Traverso, M. Ahmed, M. Garetto, P. Giaccone, E. Leonardi, and S. Niccolini, “Temporal locality in today’s content caching: why it matters and how to model it,” ACM SIGCOMM Computer Communication Review , vol. 43, no. 5, pp. 5–12, 2013
2013
Earlier work this paper cites.
K. Shanmugam, N. Golrezaei, A. G. Dimakis, A. F. Molisch, and G. Caire, “Femtocaching: Wireless content delivery through distributed caching helpers,” IEEE Transactions on Information Theory , vol. 59, no. 12, pp. 8402–8413, 2013
2013
Earlier work this paper cites.
M. Ji, G. Caire, and A. F. Molisch, “Optimal throughput-outage trade-off in wireless one-hop caching networks,” in 2013 IEEE International Symposium on Information Theory . IEEE, 2013, pp. 1461–1465
2013
Earlier work this paper cites.
M. Taghizadeh, K. Micinski, S. Biswas, C. Ofria, and E. Torng, “Distributed cooperative caching in social wireless networks,” IEEE Transactions on Mobile Computing , vol. 12, no. 6, pp. 1037–1053, 2013
2013
Earlier work this paper cites.
E. Baştuğ, J.-L. Guénégo, and M. Debbah, “Proactive small cell networks,” in ICT 2013 . IEEE, 2013, pp. 1–5
2013
Earlier work this paper cites.
C. Bo, X. Jian, X.-Y. Li, X. Mao, Y. Wang, and F. Li, “You’re driving and texting: detecting drivers using personal smart phones by leveraging inertial sensors,” in Proceedings of the 19th annual international conference on Mobile computing & networking . ACM, 2013, pp. 199–202
2013
Earlier work this paper cites.
J. Gu, W. Wang, A. Huang, and H. Shan, “Proactive storage at caching-enable base stations in cellular networks,” in 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) . IEEE, 2013, pp. 1543–1547
2013
Earlier work this paper cites.
A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE international conference on acoustics, speech and signal processing . IEEE, 2013, pp. 6645–6649
2013
Earlier work this paper cites.
M. Denil, B. Shakibi, L. Dinh, N. De Freitas et al. , “Predicting parameters in deep learning,” in Advances in neural information processing systems , 2013, pp. 2148–2156
2013
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE transactions on pattern analysis and machine intelligence , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Earlier work this paper cites.
W. Huang, G. Song, H. Hong, and K. Xie, “Deep architecture for traffic flow prediction: deep belief networks with multitask learning,” IEEE Transactions on Intelligent Transportation Systems , vol. 15, no. 5, pp. 2191–2201, 2014
2014
Earlier work this paper cites.
Y. Lv, Y. Duan, W. Kang, Z. Li, and F.-Y. Wang, “Traffic flow prediction with big data: a deep learning approach,” IEEE Transactions on Intelligent Transportation Systems , vol. 16, no. 2, pp. 865–873, 2014
2014
Earlier work this paper cites.
K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan, “Towards wearable cognitive assistance,” in Proceedings of the 12th annual international conference on Mobile systems, applications, and services , 2014, pp. 68–81
2014
Earlier work this paper cites.
H. Ahlehagh and S. Dey, “Video-aware scheduling and caching in the radio access network,” IEEE/ACM Transactions on Networking (TON) , vol. 22, no. 5, pp. 1444–1462, 2014
2014
Earlier work this paper cites.
X. Wang, M. Chen, T. Taleb, A. Ksentini, and V. C. Leung, “Cache in the air: Exploiting content caching and delivery techniques for 5g systems,” IEEE Communications Magazine , vol. 52, no. 2, pp. 131–139, 2014
2014
Earlier work this paper cites.
F. Pantisano, M. Bennis, W. Saad, and M. Debbah, “Cache-aware user association in backhaul-constrained small cell networks,” in 2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) . IEEE, 2014, pp. 37–42
2014
Earlier work this paper cites.
Y. Guan, Y. Xiao, H. Feng, C.-C. Shen, and L. J. Cimini, “Mobicacher: Mobility-aware content caching in small-cell networks,” in 2014 IEEE Global Communications Conference . IEEE, 2014, pp. 4537–4542
2014
Earlier work this paper cites.
J. Quevedo, D. Corujo, and R. Aguiar, “A case for icn usage in iot environments,” in 2014 IEEE Global Communications Conference . IEEE, 2014, pp. 2770–2775
2014
Earlier work this paper cites.
D. Malak and M. Al-Shalash, “Optimal caching for device-to-device content distribution in 5g networks,” in 2014 IEEE Globecom Workshops (GC Wkshps) . IEEE, 2014, pp. 863–868
2014
Earlier work this paper cites.
N. Naderializadeh, D. T. Kao, and A. S. Avestimehr, “How to utilize caching to improve spectral efficiency in device-to-device wireless networks,” in 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, 2014, pp. 415–422
2014
Earlier work this paper cites.
E. Bastug, M. Bennis, and M. Debbah, “Living on the edge: The role of proactive caching in 5g wireless networks,” IEEE Communications Magazine , vol. 52, no. 8, pp. 82–89, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus, “Exploiting linear structure within convolutional networks for efficient evaluation,” in Advances in neural information processing systems , 2014, pp. 1269–1277
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
D. Soudry, I. Hubara, and R. Meir, “Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights,” in Advances in Neural Information Processing Systems , 2014, pp. 963–971
2014
Earlier work this paper cites.
K. Poularakis, G. Iosifidis, V. Sourlas, and L. Tassiulas, “Multicast-aware caching for small cell networks,” in 2014 IEEE Wireless Communications and Networking Conference (WCNC) . IEEE, 2014, pp. 2300–2305
2014
Earlier work this paper cites.
D. Liu and C. Yang, “Will caching at base station improve energy efficiency of downlink transmission?” in 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) . IEEE, 2014, pp. 173–177
2014
Earlier work this paper cites.
K. Poularakis, V. Sourlas, P. Flegkas, and L. Tassiulas, “On exploiting network coding in cache-capable small-cell networks,” in 2014 IEEE Symposium on Computers and Communications (ISCC) . IEEE, 2014, pp. 1–5
2014
Earlier work this paper cites.
S. Amershi, M. Cakmak, W. B. Knox, and T. Kulesza, “Power to the people: The role of humans in interactive machine learning,” Ai Magazine , vol. 35, no. 4, pp. 105–120, 2014
2014
Earlier work this paper cites.
S. G. Klauer, F. Guo, B. G. Simons-Morton, M. C. Ouimet, S. E. Lee, and T. A. Dingus, “Distracted driving and risk of road crashes among novice and experienced drivers,” New England journal of medicine , vol. 370, no. 1, pp. 54–59, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep face recognition,” 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
P. Garcia Lopez, A. Montresor, D. Epema, A. Datta, T. Higashino, A. Iamnitchi, M. Barcellos, P. Felber, and E. Riviere, “Edge-centric computing: Vision and challenges,” ACM SIGCOMM Computer Communication Review , vol. 45, no. 5, pp. 37–42, 2015
2015
Earlier work this paper cites.
Y. C. Hu, M. Patel, D. Sabella, N. Sprecher, and V. Young, “Mobile edge computing—a key technology towards 5g,” ETSI white paper , vol. 11, no. 11, pp. 1–16, 2015
2015
Earlier work this paper cites.
S. Yi, Z. Hao, Z. Qin, and Q. Li, “Fog computing: Platform and applications,” in 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb) . IEEE, 2015, pp. 73–78
2015
Earlier work this paper cites.
N. D. Lane, S. Bhattacharya, P. Georgiev, C. Forlivesi, and F. Kawsar, “An early resource characterization of deep learning on wearables, smartphones and internet-of-things devices,” in Proceedings of the 2015 international workshop on internet of things towards applications . ACM, 2015, pp. 7–12
2015
Earlier work this paper cites.
B. Blaszczyszyn and A. Giovanidis, “Optimal geographic caching in cellular networks,” in 2015 IEEE International Conference on Communications (ICC) . IEEE, 2015, pp. 3358–3363
2015
Earlier work this paper cites.
X. Peng, J.-C. Shen, J. Zhang, and K. B. Letaief, “Backhaul-aware caching placement for wireless networks,” in 2015 IEEE Global Communications Conference (GLOBECOM) . IEEE, 2015, pp. 1–6
2015
Earlier work this paper cites.
W. C. Ao and K. Psounis, “Distributed caching and small cell cooperation for fast content delivery,” in Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing . ACM, 2015, pp. 127–136
2015
Earlier work this paper cites.
M. A. Kader, E. Bastug, M. Bennis, E. Zeydan, A. Karatepe, A. S. Er, and M. Debbah, “Leveraging big data analytics for cache-enabled wireless networks,” in 2015 IEEE Globecom Workshops (GC Wkshps) . IEEE, 2015, pp. 1–6
2015
Earlier work this paper cites.
F. Pantisano, M. Bennis, W. Saad, and M. Debbah, “Match to cache: Joint user association and backhaul allocation in cache-aware small cell networks,” in 2015 IEEE International Conference on Communications (ICC) . IEEE, 2015, pp. 3082–3087
2015
Earlier work this paper cites.
E. Baştuğ, M. Bennis, and M. Debbah, “A transfer learning approach for cache-enabled wireless networks,” in 2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) . IEEE, 2015, pp. 161–166
2015
Earlier work this paper cites.
T. Y.-H. Chen, L. Ravindranath, S. Deng, P. Bahl, and H. Balakrishnan, “Glimpse: Continuous, real-time object recognition on mobile devices,” in Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems . ACM, 2015, pp. 155–168
2015
Earlier work this paper cites.
——, “The throughput-outage tradeoff of wireless one-hop caching networks,” IEEE Transactions on Information Theory , vol. 61, no. 12, pp. 6833–6859, 2015
2015
Earlier work this paper cites.
T. Miu, P. Missier, and T. Plötz, “Bootstrapping personalised human activity recognition models using online active learning,” in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing . IEEE, 2015, pp. 1138–1147
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
L. Liu, C. Karatas, H. Li, S. Tan, M. Gruteser, J. Yang, Y. Chen, and R. P. Martin, “Toward detection of unsafe driving with wearables,” in Proceedings of the 2015 workshop on Wearable Systems and Applications . ACM, 2015, pp. 27–32
2015
Earlier work this paper cites.
N. D. Lane, P. Georgiev, and L. Qendro, “Deepear: robust smartphone audio sensing in unconstrained acoustic environments using deep learning,” in Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing . ACM, 2015, pp. 283–294
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
2015
Earlier work this paper cites.
S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,” in Advances in neural information processing systems , 2015, pp. 1135–1143
2015
Earlier work this paper cites.
W. Chen, J. Wilson, S. Tyree, K. Weinberger, and Y. Chen, “Compressing neural networks with the hashing trick,” in International Conference on Machine Learning , 2015, pp. 2285–2294
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Courbariaux, Y. Bengio, and J.-P. David, “Binaryconnect: Training deep neural networks with binary weights during propagations,” in Advances in neural information processing systems , 2015, pp. 3123–3131
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
S. Anwar, K. Hwang, and W. Sung, “Fixed point optimization of deep convolutional neural networks for object recognition,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2015, pp. 1131–1135
2015
Earlier work this paper cites.
S. K. Esser, R. Appuswamy, P. Merolla, J. V. Arthur, and D. S. Modha, “Backpropagation for energy-efficient neuromorphic computing,” in Advances in Neural Information Processing Systems , 2015, pp. 1117–1125
2015
Earlier work this paper cites.
S. S. L. Oskouei, H. Golestani, M. Kachuee, M. Hashemi, H. Mohammadzade, and S. Ghiasi, “Gpu-based acceleration of deep convolutional neural networks on mobile platforms,” Distrib. Parallel Clust. Comput , 2015
2015
Earlier work this paper cites.
D. Singh and C. K. Reddy, “A survey on platforms for big data analytics,” Journal of big data , vol. 2, no. 1, p. 8, 2015
2015
Earlier work this paper cites.
S. Yi, C. Li, and Q. Li, “A survey of fog computing: concepts, applications and issues,” in Proceedings of the 2015 workshop on mobile big data , 2015, pp. 37–42
2015
Earlier work this paper cites.
E. Bacstug, M. Bennis, M. Kountouris, and M. Debbah, “Cache-enabled small cell networks: Modeling and tradeoffs,” EURASIP Journal on Wireless Communications and Networking , vol. 2015, no. 1, p. 41, 2015
2015
Earlier work this paper cites.
Z. Chen and M. Kountouris, “Cache-enabled small cell networks with local user interest correlation,” in 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) . IEEE, 2015, pp. 680–684
2015
Earlier work this paper cites.
A. Khreishah and J. Chakareski, “Collaborative caching for multicell-coordinated systems,” in 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) . IEEE, 2015, pp. 257–262
2015
Earlier work this paper cites.
D. Wen, H. Han, and A. K. Jain, “Face spoof detection with image distortion analysis,” IEEE Transactions on Information Forensics and Security , vol. 10, no. 4, pp. 746–761, 2015
2015
Earlier work this paper cites.
A. Stisen, H. Blunck, S. Bhattacharya, T. S. Prentow, M. B. Kjærgaard, A. Dey, T. Sonne, and M. M. Jensen, “Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition,” in Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems . ACM, 2015, pp. 127–140
2015
Earlier work this paper cites.
Z. Fang, F. Fei, Y. Fang, C. Lee, N. Xiong, L. Shu, and S. Chen, “Abnormal event detection in crowded scenes based on deep learning,” Multimedia Tools and Applications , vol. 75, no. 22, pp. 14 617–14 639, 2016
2016
Earlier work this paper cites.
C. Potes, S. Parvaneh, A. Rahman, and B. Conroy, “Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds,” in 2016 Computing in Cardiology Conference (CinC) . IEEE, 2016, pp. 621–624
2016
Earlier work this paper cites.
“Cisco visual networking index: Global mobile data traffic forecast update (2017–2022),” http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html
2016
Earlier work this paper cites.
W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet of Things Journal , vol. 3, no. 5, pp. 637–646, Oct 2016
2016
Earlier work this paper cites.
S. Dernbach, N. Taft, J. Kurose, U. Weinsberg, C. Diot, and A. Ashkan, “Cache content-selection policies for streaming video services,” in IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications . IEEE, 2016, pp. 1–9
2016
Earlier work this paper cites.
D. Liu, B. Chen, C. Yang, and A. F. Molisch, “Caching at the wireless edge: design aspects, challenges, and future directions,” IEEE Communications Magazine , vol. 54, no. 9, pp. 22–28, 2016
2016
Earlier work this paper cites.
T. Li, Z. Xiao, H. M. Georges, Z. Luo, and D. Wang, “Performance analysis of co-and cross-tier device-to-device communication underlaying macro-small cell wireless networks.” KSII Transactions on Internet & Information Systems , vol. 10, no. 4, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Z. Xiao, T. Li, W. Ding, D. Wang, and J. Zhang, “Dynamic pci allocation on avoiding handover confusion via cell status prediction in lte heterogeneous small cell networks,” Wireless Communications and Mobile Computing , vol. 16, no. 14, pp. 1972–1986, 2016
2016
Earlier work this paper cites.
Z. Xiao, H. Liu, V. Havyarimana, T. Li, and D. Wang, “Analytical study on multi-tier 5g heterogeneous small cell networks: Coverage performance and energy efficiency,” Sensors , vol. 16, no. 11, p. 1854, 2016
2016
Earlier work this paper cites.
Z. Xiao, J. Yu, T. Li, Z. Xiang, D. Wang, and W. Chen, “Resource allocation via hierarchical clustering in dense small cell networks: a correlated equilibrium approach,” in 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) . IEEE, 2016, pp. 1–5
2016
Earlier work this paper cites.
K. Hamidouche, W. Saad, M. Debbah, and H. V. Poor, “Mean-field games for distributed caching in ultra-dense small cell networks,” in 2016 American Control Conference (ACC) . IEEE, 2016, pp. 4699–4704
2016
Earlier work this paper cites.
N. Zhao, X. Liu, F. R. Yu, M. Li, and V. C. Leung, “Communications, caching, and computing oriented small cell networks with interference alignment,” IEEE Communications Magazine , vol. 54, no. 9, pp. 29–35, 2016
2016
Earlier work this paper cites.
D. Liu and C. Yang, “Cache-enabled heterogeneous cellular networks: Comparison and tradeoffs,” in 2016 IEEE International Conference on Communications (ICC) . IEEE, 2016, pp. 1–6
2016
Earlier work this paper cites.
B. Chen, C. Yang, and G. Wang, “Cooperative device-to-device communications with caching,” in 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) . IEEE, 2016, pp. 1–5
2016
Earlier work this paper cites.
M. Afshang, H. S. Dhillon, and P. H. J. Chong, “Fundamentals of cluster-centric content placement in cache-enabled device-to-device networks,” IEEE Transactions on Communications , vol. 64, no. 6, pp. 2511–2526, 2016
2016
Earlier work this paper cites.
C. Jarray and A. Giovanidis, “The effects of mobility on the hit performance of cached d2d networks,” in 2016 14th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOpt) . IEEE, 2016, pp. 1–8
2016
Earlier work this paper cites.
B. Bai, L. Wang, Z. Han, W. Chen, and T. Svensson, “Caching based socially-aware d2d communications in wireless content delivery networks: A hypergraph framework,” IEEE Wireless Communications , vol. 23, no. 4, pp. 74–81, 2016
2016
Earlier work this paper cites.
Z. Chen, Y. Liu, B. Zhou, and M. Tao, “Caching incentive design in wireless d2d networks: A stackelberg game approach,” in 2016 IEEE International Conference on Communications (ICC) . IEEE, 2016, pp. 1–6
2016
Earlier work this paper cites.
D. Li, T. Salonidis, N. V. Desai, and M. C. Chuah, “Deepcham: Collaborative edge-mediated adaptive deep learning for mobile object recognition,” in 2016 IEEE/ACM Symposium on Edge Computing (SEC) . IEEE, 2016, pp. 64–76
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
J. Chen, R. Monga, S. Bengio, and R. Jozefowicz, “Revisiting distributed synchronous sgd,” 04 2016
2016
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2016, pp. 308–318
2016
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning , 2016, pp. 1050–1059
2016
Earlier work this paper cites.
S. Bhattacharya and N. D. Lane, “From smart to deep: Robust activity recognition on smartwatches using deep learning,” in 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) . IEEE, 2016, pp. 1–6
2016
Earlier work this paper cites.
V. Radu, N. D. Lane, S. Bhattacharya, C. Mascolo, M. K. Marina, and F. Kawsar, “Towards multimodal deep learning for activity recognition on mobile devices,” in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct . ACM, 2016, pp. 185–188
2016
Earlier work this paper cites.
P. Wang and J. Cheng, “Accelerating convolutional neural networks for mobile applications,” in Proceedings of the 24th ACM international conference on Multimedia . ACM, 2016, pp. 541–545
2016
Earlier work this paper cites.
S. Bhattacharya and N. D. Lane, “Sparsification and separation of deep learning layers for constrained resource inference on wearables,” in Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM . ACM, 2016, pp. 176–189
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
C.-F. Chen, G. G. Lee, V. Sritapan, and C.-Y. Lin, “Deep convolutional neural network on ios mobile devices,” in 2016 IEEE International Workshop on Signal Processing Systems (SiPS) . IEEE, 2016, pp. 130–135
2016
Earlier work this paper cites.
J. Wu, C. Leng, Y. Wang, Q. Hu, and J. Cheng, “Quantized convolutional neural networks for mobile devices,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4820–4828
2016
Cited alongside, same era.
2016
Cited alongside, same era.
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, “Xnor-net: Imagenet classification using binary convolutional neural networks,” in European Conference on Computer Vision . Springer, 2016, pp. 525–542
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S. S. Ogden and T. Guo, “ { \{ MODI } \} : Mobile deep inference made efficient by edge computing,” in { \{ USENIX } \} Workshop on Hot Topics in Edge Computing (HotEdge 18) , 2018
2018
Later among the works it cites.
S. Peng, L. Li, X. Tan, G. Zhao, and Z. Chen, “Optimal caching strategy in device-to-device wireless networks,” in 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) . IEEE, 2018, pp. 78–82
2018
Later among the works it cites.
Y. Chen, S. Biookaghazadeh, and M. Zhao, “Exploring the capabilities of mobile devices supporting deep learning,” in Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing . ACM, 2018, pp. 17–18
2018
Later among the works it cites.
Y. Huang, Y. Zhu, X. Fan, X. Ma, F. Wang, J. Liu, Z. Wang, and Y. Cui, “Task scheduling with optimized transmission time in collaborative cloud-edge learning,” in 2018 27th International Conference on Computer Communication and Networks (ICCCN) . IEEE, 2018, pp. 1–9
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. S. Latifi Oskouei, H. Golestani, M. Hashemi, and S. Ghiasi, “Cnndroid: Gpu-accelerated execution of trained deep convolutional neural networks on android,” in Proceedings of the 24th ACM international conference on Multimedia . ACM, 2016, pp. 1201–1205
2016
Cited alongside, same era.
P.-K. Tsung, S.-F. Tsai, A. Pai, S.-J. Lai, and C. Lu, “High performance deep neural network on low cost mobile gpu,” in 2016 IEEE International Conference on Consumer Electronics (ICCE) . IEEE, 2016, pp. 69–70
2016
Cited alongside, same era.
S. Rizvi, G. Cabodi, D. Patti, and G. Francini, “Gpgpu accelerated deep object classification on a heterogeneous mobile platform,” Electronics , vol. 5, no. 4, p. 88, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
L. N. Huynh, R. K. Balan, and Y. Lee, “Deepsense: A gpu-based deep convolutional neural network framework on commodity mobile devices,” in Proceedings of the 2016 Workshop on Wearable Systems and Applications . ACM, 2016, pp. 25–30
2016
Cited alongside, same era.
S. Rallapalli, H. Qiu, A. Bency, S. Karthikeyan, R. Govindan, B. Manjunath, and R. Urgaonkar, “Are very deep neural networks feasible on mobile devices,” IEEE Trans. Circ. Syst. Video Technol , 2016
2016
Cited alongside, same era.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788
2016
Cited alongside, same era.
Y.-H. Chen, T. Krishna, J. S. Emer, and V. Sze, “Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks,” IEEE Journal of Solid-State Circuits , vol. 52, no. 1, pp. 127–138, 2016
2016
Cited alongside, same era.
2018
Later among the works it cites.
T. Xing, S. S. Sandha, B. Balaji, S. Chakraborty, and M. Srivastava, “Enabling edge devices that learn from each other: Cross modal training for activity recognition,” in Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking . ACM, 2018, pp. 37–42
2018
Later among the works it cites.
——, “Low precision deep learning training on mobile heterogeneous platform,” in 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) . IEEE, 2018, pp. 109–117
2018
Later among the works it cites.
S. Flutura, A. Seiderer, I. Aslan, C.-T. Dang, R. Schwarz, D. Schiller, and E. André, “Drinkwatch: A mobile wellbeing application based on interactive and cooperative machine learning,” in Proceedings of the 2018 International Conference on Digital Health . ACM, 2018, pp. 65–74
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
D. Yin, Y. Chen, R. Kannan, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. Stockholmsmässan, Stockholm Sweden: PMLR, 10–15 Jul 2018, pp. 5650–5659
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Yao, Y. Zhao, H. Shao, A. Zhang, C. Zhang, S. Li, and T. Abdelzaher, “Rdeepsense: Reliable deep mobile computing models with uncertainty estimations,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 1, no. 4, p. 173, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
M. J. Sheller, G. A. Reina, B. Edwards, J. Martin, and S. Bakas, “Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation,” in International MICCAI Brainlesion Workshop . Springer, 2018, pp. 92–104
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Chen, Y. Liu, X. Gao, and Z. Han, “Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices,” in Chinese Conference on Biometric Recognition . Springer, 2018, pp. 428–438
2018
Later among the works it cites.
2018
Later among the works it cites.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8697–8710
2018
Later among the works it cites.
2018
Later among the works it cites.
X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 6848–6856
2018
Later among the works it cites.
Z. Qin, Z. Zhang, S. Zhang, H. Yu, and Y. Peng, “Merging-and-evolution networks for mobile vision applications,” IEEE Access , vol. 6, pp. 31 294–31 306, 2018
2018
Later among the works it cites.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4510–4520
2018
Later among the works it cites.
B. Almaslukh, J. Al Muhtadi, and A. M. Artoli, “A robust convolutional neural network for online smartphone-based human activity recognition,” Journal of Intelligent & Fuzzy Systems , no. Preprint, pp. 1–12, 2018
2018
Later among the works it cites.
B. Almaslukh, A. Artoli, and J. Al-Muhtadi, “A robust deep learning approach for position-independent smartphone-based human activity recognition,” Sensors , vol. 18, no. 11, p. 3726, 2018
2018
Later among the works it cites.
P. Sundaramoorthy, G. K. Gudur, M. R. Moorthy, R. N. Bhandari, and V. Vijayaraghavan, “Harnet: Towards on-device incremental learning using deep ensembles on constrained devices,” in Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning . ACM, 2018, pp. 31–36
2018
Later among the works it cites.
F. Cruciani, I. Cleland, C. Nugent, P. McCullagh, K. Synnes, and J. Hallberg, “Automatic annotation for human activity recognition in free living using a smartphone,” Sensors , vol. 18, no. 7, p. 2203, 2018
2018
Later among the works it cites.
X. Bo, C. Poellabauer, M. K. O’Brien, C. K. Mummidisetty, and A. Jayaraman, “Detecting label errors in crowd-sourced smartphone sensor data,” in 2018 International Workshop on Social Sensing (SocialSens) . IEEE, 2018, pp. 20–25
2018
Later among the works it cites.
S. Yao, Y. Zhao, S. Hu, and T. Abdelzaher, “Qualitydeepsense: Quality-aware deep learning framework for internet of things applications with sensor-temporal attention,” in Proceedings of the 2nd International Workshop on Embedded and Mobile Deep Learning . ACM, 2018, pp. 42–47
2018
Later among the works it cites.
E. J. Crowley, G. Gray, and A. J. Storkey, “Moonshine: Distilling with cheap convolutions,” in Advances in Neural Information Processing Systems , 2018, pp. 2888–2898
2018
Later among the works it cites.
D. Li, X. Wang, and D. Kong, “Deeprebirth: Accelerating deep neural network execution on mobile devices,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Later among the works it cites.
G. Zhou, Y. Fan, R. Cui, W. Bian, X. Zhu, and K. Gai, “Rocket launching: A universal and efficient framework for training well-performing light net,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Shen, T. Han, Q. Yang, X. Yang, Y. Wang, F. Li, and H. Wen, “Cs-cnn: Enabling robust and efficient convolutional neural networks inference for internet-of-things applications,” IEEE Access , vol. 6, pp. 13 439–13 448, 2018
2018
Later among the works it cites.
A. Gordon, E. Eban, O. Nachum, B. Chen, H. Wu, T.-J. Yang, and E. Choi, “Morphnet: Fast & simple resource-constrained structure learning of deep networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 1586–1595
2018
Later among the works it cites.
F. Manessi, A. Rozza, S. Bianco, P. Napoletano, and R. Schettini, “Automated pruning for deep neural network compression,” in 2018 24th International Conference on Pattern Recognition (ICPR) . IEEE, 2018, pp. 657–664
2018
Later among the works it cites.
S. H. F. Langroudi, T. Pandit, and D. Kudithipudi, “Deep learning inference on embedded devices: Fixed-point vs posit,” in 2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) . IEEE, 2018, pp. 19–23
2018
Later among the works it cites.
P. Wang, Q. Hu, Z. Fang, C. Zhao, and J. Cheng, “Deepsearch: A fast image search framework for mobile devices,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) , vol. 14, no. 1, p. 6, 2018
2018
Later among the works it cites.
S. Liu, Y. Lin, Z. Zhou, K. Nan, H. Liu, and J. Du, “On-demand deep model compression for mobile devices: A usage-driven model selection framework,” in Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services . ACM, 2018, pp. 389–400
2018
Later among the works it cites.
M. Loukadakis, J. Cano, and M. O’Boyle, “Accelerating deep neural networks on low power heterogeneous architectures,” 2018
2018
Later among the works it cites.
——, “Cappuccino: Efficient cnn inference software synthesis for mobile system-on-chips,” IEEE Embedded Systems Letters , vol. 11, no. 1, pp. 9–12, 2018
2018
Later among the works it cites.
S.-S. Park, K.-B. Park, and K.-S. Chung, “Implementation of a cnn accelerator on an embedded soc platform using sdsoc,” in Proceedings of the 2nd International Conference on Digital Signal Processing . ACM, 2018, pp. 161–165
2018
Later among the works it cites.
Y.-H. Chen, T.-J. Yang, J. Emer, and V. Sze, “Understanding the limitations of existing energy-efficient design approaches for deep neural networks,” Energy , vol. 2, no. L1, p. L3, 2018
2018
Later among the works it cites.
T.-J. Yang, A. Howard, B. Chen, X. Zhang, A. Go, M. Sandler, V. Sze, and H. Adam, “Netadapt: Platform-aware neural network adaptation for mobile applications,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 285–300
2018
Later among the works it cites.
T. Abtahi, C. Shea, A. Kulkarni, and T. Mohsenin, “Accelerating convolutional neural network with fft on embedded hardware,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems , vol. 26, no. 9, pp. 1737–1749, 2018
2018
Later among the works it cites.
H. Li, K. Ota, and M. Dong, “Learning iot in edge: Deep learning for the internet of things with edge computing,” IEEE Network , vol. 32, no. 1, pp. 96–101, 2018
2018
Later among the works it cites.
A. E. Eshratifar and M. Pedram, “Energy and performance efficient computation offloading for deep neural networks in a mobile cloud computing environment,” in Proceedings of the 2018 on Great Lakes Symposium on VLSI . ACM, 2018, pp. 111–116
2018
Later among the works it cites.
M. Ali, A. Anjum, M. U. Yaseen, A. R. Zamani, D. Balouek-Thomert, O. Rana, and M. Parashar, “Edge enhanced deep learning system for large-scale video stream analytics,” in 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC) . IEEE, 2018, pp. 1–10
2018
Later among the works it cites.
P. Sanabria, J. I. Benedetto, A. Neyem, J. Navon, and C. Poellabauer, “Code offloading solutions for audio processing in mobile healthcare applications: a case study,” in 2018 IEEE/ACM 5th International Conference on Mobile Software Engineering and Systems (MOBILESoft) . IEEE, 2018, pp. 117–121
2018
Later among the works it cites.
J. Hanhirova, T. Kämäräinen, S. Seppälä, M. Siekkinen, V. Hirvisalo, and A. Ylä-Jääski, “Latency and throughput characterization of convolutional neural networks for mobile computer vision,” in Proceedings of the 9th ACM Multimedia Systems Conference . ACM, 2018, pp. 204–215
2018
Later among the works it cites.
X. Ran, H. Chen, X. Zhu, Z. Liu, and J. Chen, “Deepdecision: A mobile deep learning framework for edge video analytics,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications . IEEE, 2018, pp. 1421–1429
2018
Later among the works it cites.
J. H. Ko, T. Na, M. F. Amir, and S. Mukhopadhyay, “Edge-host partitioning of deep neural networks with feature space encoding for resource-constrained internet-of-things platforms,” in 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) . IEEE, 2018, pp. 1–6
2018
Later among the works it cites.
H.-J. Jeong, I. Jeong, H.-J. Lee, and S.-M. Moon, “Computation offloading for machine learning web apps in the edge server environment,” in 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) . IEEE, 2018, pp. 1492–1499
2018
Later among the works it cites.
P. Liu, B. Qi, and S. Banerjee, “Edgeeye: An edge service framework for real-time intelligent video analytics,” in Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking . ACM, 2018, pp. 1–6
2018
Later among the works it cites.
M. Song, K. Zhong, J. Zhang, Y. Hu, D. Liu, W. Zhang, J. Wang, and T. Li, “In-situ ai: Towards autonomous and incremental deep learning for iot systems,” in 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA) . IEEE, 2018, pp. 92–103
2018
Later among the works it cites.
2018
Later among the works it cites.
N. Talagala, S. Sundararaman, V. Sridhar, D. Arteaga, Q. Luo, S. Subramanian, S. Ghanta, L. Khermosh, and D. Roselli, “ { \{ ECO } \} : Harmonizing edge and cloud with ml/dl orchestration,” in { \{ USENIX } \} Workshop on Hot Topics in Edge Computing (HotEdge 18) , 2018
2018
Later among the works it cites.
E. De Coninck, S. Bohez, S. Leroux, T. Verbelen, B. Vankeirsbilck, P. Simoens, and B. Dhoedt, “Dianne: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure,” Journal of Systems and Software , vol. 141, pp. 52–65, 2018
2018
Later among the works it cites.
Y. Fukushima, D. Miura, T. Hamatani, H. Yamaguchi, and T. Higashino, “Microdeep: In-network deep learning by micro-sensor coordination for pervasive computing,” in 2018 IEEE International Conference on Smart Computing (SMARTCOMP) . IEEE, 2018, pp. 163–170
2018
Later among the works it cites.
L. Li, K. Ota, and M. Dong, “Deep learning for smart industry: Efficient manufacture inspection system with fog computing,” IEEE Transactions on Industrial Informatics , vol. 14, no. 10, pp. 4665–4673, 2018
2018
Later among the works it cites.
T. Muhammed, R. Mehmood, A. Albeshri, and I. Katib, “Ubehealth: a personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities,” IEEE Access , vol. 6, pp. 32 258–32 285, 2018
2018
Later among the works it cites.
W. Zhang, B. Han, and P. Hui, “Jaguar: Low latency mobile augmented reality with flexible tracking,” in Proceedings of the 26th ACM international conference on Multimedia , 2018, pp. 355–363
2018
Later among the works it cites.
G. S. Paschos, G. Iosifidis, M. Tao, D. Towsley, and G. Caire, “The role of caching in future communication systems and networks,” IEEE Journal on Selected Areas in Communications , vol. 36, no. 6, pp. 1111–1125, 2018
2018
Later among the works it cites.
L. Li, G. Zhao, and R. S. Blum, “A survey of caching techniques in cellular networks: Research issues and challenges in content placement and delivery strategies,” IEEE Communications Surveys & Tutorials , vol. 20, no. 3, pp. 1710–1732, 2018
2018
Later among the works it cites.
L. Lovagnini, W. Zhang, F. H. Bijarbooneh, and P. Hui, “Circe: Real-time caching for instance recognition on cloud environments and multi-core architectures,” in Proceedings of the 26th ACM international conference on Multimedia , 2018, pp. 346–354
2018
Later among the works it cites.
J. Li, C. Shunfeng, F. Shu, J. Wu, and D. N. K. Jayakody, “Contract-based small-cell caching for data disseminations in ultra-dense cellular networks,” IEEE Transactions on Mobile Computing , 2018
2018
Later among the works it cites.
M. Lavassani, S. Forsström, U. Jennehag, and T. Zhang, “Combining fog computing with sensor mote machine learning for industrial iot,” Sensors , vol. 18, no. 5, p. 1532, 2018
2018
Later among the works it cites.
S. Yao, Y. Zhao, H. Shao, C. Zhang, A. Zhang, D. Liu, S. Liu, L. Su, and T. Abdelzaher, “Apdeepsense: Deep learning uncertainty estimation without the pain for iot applications,” in 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) . IEEE, 2018, pp. 334–343
2018
Later among the works it cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
Later among the works it cites.
T. Guo, “Cloud-based or on-device: An empirical study of mobile deep inference,” in 2018 IEEE International Conference on Cloud Engineering (IC2E) . IEEE, 2018, pp. 184–190
2018
Later among the works it cites.
2018
Later among the works it cites.
T. Plötz and Y. Guan, “Deep learning for human activity recognition in mobile computing,” Computer , vol. 51, no. 5, pp. 50–59, 2018
2018
Later among the works it cites.
A. Rosenfeld and J. K. Tsotsos, “Incremental learning through deep adaptation,” IEEE transactions on pattern analysis and machine intelligence , 2018
2018
Later among the works it cites.
X. Xu, Y. Ding, S. X. Hu, M. Niemier, J. Cong, Y. Hu, and Y. Shi, “Scaling for edge inference of deep neural networks,” Nature Electronics , vol. 1, no. 4, p. 216, 2018
2018
Later among the works it cites.
D. Xu, Y. Li, X. Chen, J. Li, P. Hui, S. Chen, and J. Crowcroft, “A survey of opportunistic offloading,” IEEE Communications Surveys & Tutorials , vol. 20, no. 3, pp. 2198–2236, 2018
2018
Later among the works it cites.
A. Thomas, Y. Guo, Y. Kim, B. Aksanli, A. Kumar, and T. S. Rosing, “Pushing down machine learning inference to the edge in heterogeneous internet of things applications,” 2018
2018
Later among the works it cites.
A. Mathur, T. Zhang, S. Bhattacharya, P. Velickovic, L. Joffe, N. D. Lane, F. Kawsar, and P. Lió, “Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices,” in 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) . IEEE, 2018, pp. 200–211
2018
Later among the works it cites.
A. Mathur, A. Isopoussu, F. Kawsar, R. Smith, N. D. Lane, and N. Berthouze, “On robustness of cloud speech apis: An early characterization,” in Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers . ACM, 2018, pp. 1409–1413
2018
Later among the works it cites.
X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, “In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning,” IEEE Network , vol. 33, no. 5, pp. 156–165, 2019
2019
Later among the works it cites.
Z. Wang, Y. Cui, and Z. Lai, “A first look at mobile intelligence: Architecture, experimentation and challenges,” IEEE Network , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 10, no. 2, p. 12, 2019
2019
Later among the works it cites.
C. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless networking: A survey,” IEEE Communications Surveys & Tutorials , 2019
2019
Later among the works it cites.
Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, “Edge intelligence: Paving the last mile of artificial intelligence with edge computing,” Proceedings of the IEEE , vol. 107, no. 8, pp. 1738–1762, 2019
2019
Later among the works it cites.
Google Street View Image API, https://developers.google.com/maps/ documentation/streetview/intro , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
W. K. Lai, C.-S. Shieh, C.-S. Ho, and Y.-R. Chen, “A clustering-based energy saving scheme for dense small cell networks,” IEEE Access , vol. 7, pp. 2880–2893, 2019
2019
Later among the works it cites.
P. Cheng, C. Ma, M. Ding, Y. Hu, Z. Lin, Y. Li, and B. Vucetic, “Localized small cell caching: A machine learning approach based on rating data,” IEEE Transactions on Communications , vol. 67, no. 2, pp. 1663–1676, 2019
2019
Later among the works it cites.
L. Cavigelli and L. Benini, “Cbinfer: Exploiting frame-to-frame locality for faster convolutional network inference on video streams,” IEEE Transactions on Circuits and Systems for Video Technology , 2019
2019
Later among the works it cites.
L. Qiu and G. Cao, “Popularity-aware caching increases the capacity of wireless networks,” IEEE Transactions on Mobile Computing , 2019
2019
Later among the works it cites.
X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, “In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning,” IEEE Network , vol. 33, no. 5, pp. 156–165, 2019
2019
Later among the works it cites.
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” IEEE Journal on Selected Areas in Communications , vol. 37, no. 6, pp. 1205–1221, 2019
2019
Later among the works it cites.
W. Yang, S. Wang, J. Hu, G. Zheng, J. Yang, and C. Valli, “Securing deep learning based edge finger-vein biometrics with binary decision diagram,” IEEE Transactions on Industrial Informatics , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge,” in ICC 2019-2019 IEEE International Conference on Communications (ICC) . IEEE, 2019, pp. 1–7
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Distributed federated learning for ultra-reliable low-latency vehicular communications,” IEEE Transactions on Communications , 2019
2019
Later among the works it cites.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Regularized evolution for image classifier architecture search,” in Proceedings of the aaai conference on artificial intelligence , vol. 33, 2019, pp. 4780–4789
2019
Later among the works it cites.
M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Q. V. Le, “Mnasnet: Platform-aware neural architecture search for mobile,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
Later among the works it cites.
D. Wofk, F. Ma, T.-J. Yang, S. Karaman, and V. Sze, “Fastdepth: Fast monocular depth estimation on embedded systems,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 6101–6108
2019
Later among the works it cites.
A. Wong, M. Famuori, M. J. Shafiee, F. Li, B. Chwyl, and J. Chung, “Yolo nano: a highly compact you only look once convolutional neural network for object detection,” 2019
2019
Later among the works it cites.
K. Yang, T. Xing, Y. Liu, Z. Li, X. Gong, X. Chen, and D. Fang, “Cdeeparch: a compact deep neural network architecture for mobile sensing,” IEEE/ACM Transactions on Networking , 2019
2019
Later among the works it cites.
Z. You, K. Yan, J. Ye, M. Ma, and P. Wang, “Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks,” in Advances in Neural Information Processing Systems 32 . Curran Associates, Inc., 2019, pp. 2133–2144
2019
Later among the works it cites.
Y.-H. Chen, T.-J. Yang, J. Emer, and V. Sze, “Eyeriss v2: A flexible accelerator for emerging deep neural networks on mobile devices,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems , 2019
2019
Later among the works it cites.
C. Xu, J. Ren, L. She, Y. Zhang, Z. Qin, and K. Ren, “Edgesanitizer: Locally differentially private deep inference at the edge for mobile data analytics,” IEEE Internet of Things Journal , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
C. Zhang and Z. Zheng, “Task migration for mobile edge computing using deep reinforcement learning,” Future Generation Computer Systems , vol. 96, pp. 111–118, 2019
2019
Later among the works it cites.
A. Yousefpour, S. Devic, B. Q. Nguyen, A. Kreidieh, A. Liao, A. M. Bayen, and J. P. Jue, “Guardians of the deep fog: Failure-resilient dnn inference from edge to cloud,” in Proceedings of the First International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things , 2019, pp. 25–31
2019
Later among the works it cites.
A. Ferdowsi, U. Challita, and W. Saad, “Deep learning for reliable mobile edge analytics in intelligent transportation systems: An overview,” ieee vehicular technology magazine , vol. 14, no. 1, pp. 62–70, 2019
2019
Later among the works it cites.
Google Glass, https://en.wikipedia.org/wiki/Google_Glass , 2019
2019
Later among the works it cites.
Microsoft Hololens, https://en.wikipedia.org/wiki/Microsoft_ HoloLens , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
B. Blanco-Filgueira, D. García-Lesta, M. Fernández-Sanjurjo, V. M. Brea, and P. López, “Deep learning-based multiple object visual tracking on embedded system for iot and mobile edge computing applications,” IEEE Internet of Things Journal , 2019
2019
Later among the works it cites.
K. A. Shatilov, D. Chatzopoulos, A. W. T. Hang, and P. Hui, “Using deep learning and mobile offloading to control a 3d-printed prosthetic hand,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 3, no. 3, pp. 1–19, 2019
2019
Later among the works it cites.
2020
Closest in time.
X. Wang, Y. Han, V. C. Leung, D. Niyato, X. Yan, and X. Chen, “Convergence of edge computing and deep learning: A comprehensive survey,” IEEE Communications Surveys & Tutorials , 2020
2020
Closest in time.
G. Zhu, D. Liu, Y. Du, C. You, J. Zhang, and K. Huang, “Toward an intelligent edge: Wireless communication meets machine learning,” IEEE Communications Magazine , vol. 58, no. 1, pp. 19–25, 2020
2020
Closest in time.
S. Ambrogio, P. Narayanan, H. Tsai, C. Mackin, K. Spoon, A. Chen, A. Fasoli, A. Friz, and G. W. Burr, “Accelerating deep neural networks with analog memory devices,” in 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) , 2020, pp. 149–152
2020
Closest in time.
H. Zeng and V. Prasanna, “Graphact: Accelerating gcn training on cpu-fpga heterogeneous platforms,” in The 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays , ser. FPGA ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 255–265. [Online]. Available: https://doi.org/10.1145/3373087.3375312
2020
Closest in time.
S. R. Pandey, N. H. Tran, M. Bennis, Y. K. Tun, A. Manzoor, and C. S. Hong, “A crowdsourcing framework for on-device federated learning,” IEEE Transactions on Wireless Communications , vol. 19, no. 5, pp. 3241–3256, 2020
2020
Closest in time.
Y. Zhan, P. Li, Z. Qu, D. Zeng, and S. Guo, “A learning-based incentive mechanism for federated learning,” IEEE Internet of Things Journal , pp. 1–1, 2020
2020
Closest in time.
K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via over-the-air computation,” IEEE Transactions on Wireless Communications , vol. 19, no. 3, pp. 2022–2035, 2020
2020
Closest in time.
F. Ang, L. Chen, N. Zhao, Y. Chen, W. Wang, and F. R. Yu, “Robust federated learning with noisy communication,” IEEE Transactions on Communications , pp. 1–1, 2020
2020
Closest in time.
M. M. Amiri and D. Gündüz, “Federated learning over wireless fading channels,” IEEE Transactions on Wireless Communications , vol. 19, no. 5, pp. 3546–3557, 2020
2020
Closest in time.
S. Savazzi, M. Nicoli, and V. Rampa, “Federated learning with cooperating devices: A consensus approach for massive iot networks,” IEEE Internet of Things Journal , vol. 7, no. 5, pp. 4641–4654, 2020
2020
Closest in time.
K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. S. Quek, and H. V. Poor, “Federated learning with differential privacy: Algorithms and performance analysis,” IEEE Transactions on Information Forensics and Security , pp. 1–1, 2020
2020
Closest in time.
J.-H. Luo and J. Wu, “Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference,” Pattern Recognition , p. 107461, 2020. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0031320320302648
2020
Closest in time.
J. Guo, W. Zhang, W. Ouyang, and D. Xu, “Model compression using progressive channel pruning,” IEEE Transactions on Circuits and Systems for Video Technology , pp. 1–1, 2020
2020
Closest in time.
O. Oyedotun, D. Aouada, and B. Ottersten, “Structured compression of deep neural networks with debiased elastic group lasso,” in The IEEE Winter Conference on Applications of Computer Vision (WACV) , March 2020
2020
Closest in time.
P. Singh, V. K. Verma, P. Rai, and V. Namboodiri, “Leveraging filter correlations for deep model compression,” in The IEEE Winter Conference on Applications of Computer Vision (WACV) , March 2020
2020
Closest in time.
J. Wang, H. Bai, J. Wu, and J. Cheng, “Bayesian automatic model compression,” IEEE Journal of Selected Topics in Signal Processing , pp. 1–1, 2020
2020
Closest in time.
Z. Liu, P. N. Whatmough, and M. Mattina, “Systolic tensor array: An efficient structured-sparse gemm accelerator for mobile cnn inference,” IEEE Computer Architecture Letters , vol. 19, no. 1, pp. 34–37, 2020
2020
Closest in time.
S. A. Osia, A. S. Shamsabadi, S. Sajadmanesh, A. Taheri, K. Katevas, H. R. Rabiee, N. D. Lane, and H. Haddadi, “A hybrid deep learning architecture for privacy-preserving mobile analytics,” IEEE Internet of Things Journal , 2020
2020
Closest in time.
T. Braud, P. Zhou, J. Kangasharju, and P. Hui, “Multipath computation offloading for mobile augmented reality,” in In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2020), Austin USA , 2020
2020
Closest in time.
C. Ma, J. Li, M. Ding, H. H. Yang, F. Shu, T. Q. S. Quek, and H. V. Poor, “On safeguarding privacy and security in the framework of federated learning,” IEEE Network , pp. 1–7, 2020
2020
Closest in time.
B. Fan, X. Liu, X. Su, J. Niu, and P. Hui, “Emgauth: An emg-based smartphone unlocking system using siamese network,” in In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2020), Austin USA . IEEE, 2020
2020
Closest in time.
V. S. Marco, B. Taylor, Z. Wang, and Y. Elkhatib, “Optimizing deep learning inference on embedded systems through adaptive model selection,” ACM Trans. Embed. Comput. Syst. , vol. 19, no. 1, Feb. 2020. [Online]. Available: https://doi.org/10.1145/3371154
2020
Closest in time.
A. Garofalo, M. Rusci, F. Conti, D. Rossi, and L. Benini, “Pulp-nn: accelerating quantized neural networks on parallel ultra-low-power risc-v processors,” Philosophical Transactions of the Royal Society A , vol. 378, no. 2164, p. 20190155, 2020
2020
Closest in time.