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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
Explaining the Gibbs sampler
George Casella and Edward I George · 1992
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi · 1994
Earlier work this paper cites.
Convolutional networks for images, speech, and time series
Yann LeCun, Yoshua Bengio, et al · 1995
Earlier work this paper cites.
No free lunch theorems for optimization
David H Wolpert and William G Macready · 1997
Earlier work this paper cites.
Learning to forget: Continual prediction with LSTM
Felix A Gers, Jürgen Schmidhuber, and Fred Cummins · 1999
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
Earlier work this paper cites.
Geometric data analysis: from correspondence analysis to structured data analysis
Brigitte Le Roux and Henry Rouanet · 2004
Earlier work this paper cites.
Links between perceptrons, MLPs and SVMs
Ronan Collobert and Samy Bengio · 2004
Earlier work this paper cites.
Core vector machines: Fast SVM training on very large data sets
Ivor W Tsang, James T Kwok, and Pak-Ming Cheung · 2005
Earlier work this paper cites.
The Horus WLAN location determination system
Moustafa Youssef and Ashok Agrawala · 2005
Earlier work this paper cites.
Statistical learning theory for location fingerprinting in wireless LANs
Mauro Brunato and Roberto Battiti · 2005
Earlier work this paper cites.
Learning a similarity metric discriminatively, with application to face verification
Sumit Chopra, Raia Hadsell, and Yann LeCun · 2005
Earlier work this paper cites.
Gaussian processes for machine learning
Carl Edward Rasmussen and Christopher KI Williams · 2006
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006
Earlier work this paper cites.
One-shot learning of object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2006
Earlier work this paper cites.
Traffic prediction for mobile network using holt-winter’s exponential smoothing
Denis Tikunov and Toshikazu Nishimura · 2007
Earlier work this paper cites.
Representational power of restricted boltzmann machines and deep belief networks
Nicolas Le Roux and Yoshua Bengio · 2008
Earlier work this paper cites.
Scalable parallel programming with CUDA
John Nickolls, Ian Buck, Michael Garland, and Kevin Skadron · 2008
Earlier work this paper cites.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y Ng · 2009
Earlier work this paper cites.
Learning deep architectures for AI
Yoshua Bengio et al · 2009
Earlier work this paper cites.
A detailed analysis of the kdd cup 99 data set
Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A Ghorbani · 2009
Earlier work this paper cites.
Zero-shot learning with semantic output codes
Mark Palatucci, Dean Pomerleau, Geoffrey E Hinton, and Tom M Mitchell · 2009
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
Earlier work this paper cites.
Torch7: A Matlab-like environment for machine learning
R. Collobert, K. Kavukcuoglu, and C. Farabet · 2011
Earlier work this paper cites.
Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
Earlier work this paper cites.
Making deep belief networks effective for large vocabulary continuous speech recognition
Tara N Sainath, Brian Kingsbury, Bhuvana Ramabhadran, Petr Fousek, Petr Novak, and Abdel-rahman Mohamed · 2011
Earlier work this paper cites.
Multimodal deep learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng · 2011
Earlier work this paper cites.
An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel
Qingjiang Shi, Meisam Razaviyayn, Zhi-Quan Luo, and Chen He · 2011
Earlier work this paper cites.
Hogwild: A lock-free approach to parallelizing stochastic gradient descent
Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu · 2011
Earlier work this paper cites.
Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for Mobile WiMAX
Hyun-Woo Kim, Jun-Hui Lee, Yong-Hoon Choi, Young-Uk Chung, and Hyukjoon Lee · 2011
Earlier work this paper cites.
Deep learning for NLP (without magic)
Richard Socher, Yoshua Bengio, and Christopher D Manning · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
A few useful things to know about machine learning
Pedro Domingos · 2012
Earlier work this paper cites.
Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, et al · 2012
Earlier work this paper cites.
ADADELTA: an adaptive learning rate method
Matthew D Zeiler · 2012
Earlier work this paper cites.
FIFS: Fine-grained indoor fingerprinting system
Jiang Xiao, Kaishun Wu, Youwen Yi, and Lionel M Ni · 2012
Earlier work this paper cites.
A survey on machine-learning techniques in cognitive radios
Mario Bkassiny, Yang Li, and Sudharman K Jayaweera · 2013
Earlier work this paper cites.
Deep Gaussian processes
Andreas Damianou and Neil Lawrence · 2013
Earlier work this paper cites.
Zero-shot learning through cross-modal transfer
Richard Socher, Milind Ganjoo, Christopher D Manning, and Andrew Ng · 2013
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George E Dahl, and Geoffrey E Hinton · 2013
Earlier work this paper cites.
MLbase: A distributed machine-learning system
Tim Kraska, Ameet Talwalkar, John C Duchi, Rean Griffith, Michael J Franklin, and Michael I Jordan · 2013
Earlier work this paper cites.
3D convolutional neural networks for human action recognition
Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu · 2013
Earlier work this paper cites.
Stochastic ratio matching of RBMs for sparse high-dimensional inputs
Yann Dauphin and Yoshua Bengio · 2013
Earlier work this paper cites.
Hybrid speech recognition with deep bidirectional LSTM
Alex Graves, Navdeep Jaitly, and Abdel-rahman Mohamed · 2013
Earlier work this paper cites.
Survey and taxonomy of botnet research through life-cycle
Rafael A Rodríguez-Gómez, Gabriel Maciá-Fernández, and Pedro García-Teodoro · 2013
Earlier work this paper cites.
Deep learning: methods and applications
Li Deng, Dong Yu, et al · 2014
Earlier work this paper cites.
A tutorial survey of architectures, algorithms, and applications for deep learning
Li Deng · 2014
Earlier work this paper cites.
Big data deep learning: challenges and perspectives
Xue-Wen Chen and Xiaotong Lin · 2014
Earlier work this paper cites.
Machine learning in wireless sensor networks: Algorithms, strategies, and applications
Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, and Hwee-Pink Tan · 2014
Earlier work this paper cites.
Data mining for Internet of things: A survey
Chun-Wei Tsai, Chin-Feng Lai, Ming-Chao Chiang, Laurence T Yang, et al · 2014
Earlier work this paper cites.
What will 5G be?
Jeffrey G Andrews, Stefano Buzzi, Wan Choi, Stephen V Hanly, Angel Lozano, Anthony CK Soong, and Jianzhong Charlie Zhang · 2014
Earlier work this paper cites.
Challenges in 5G: how to empower SON with big data for enabling 5G
Ali Imran, Ahmed Zoha, and Adnan Abu-Dayya · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
Earlier work this paper cites.
cuDNN: Efficient primitives for deep learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
Earlier work this paper cites.
A 240 G-ops/s mobile coprocessor for deep neural networks
Vinayak Gokhale, Jonghoon Jin, Aysegul Dundar, Berin Martini, and Eugenio Culurciello · 2014
Earlier work this paper cites.
Project adam: Building an efficient and scalable deep learning training system
Trishul M Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman · 2014
Earlier work this paper cites.
Fog computing: A platform for internet of things and analytics
Flavio Bonomi, Rodolfo Milito, Preethi Natarajan, and Jiang Zhu · 2014
Earlier work this paper cites.
Finding your way in the fog: Towards a comprehensive definition of fog computing
Luis M Vaquero and Luis Rodero-Merino · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
Forecast chaotic time series data by DBNs
Takashi Kuremoto, Masanao Obayashi, Kunikazu Kobayashi, Takaomi Hirata, and Shingo Mabu · 2014
Earlier work this paper cites.
Anomaly detection using autoencoders with nonlinear dimensionality reduction
Mayu Sakurada and Takehisa Yairi · 2014
Earlier work this paper cites.
Tact: A transfer actor-critic learning framework for energy saving in cellular radio access networks
Rongpeng Li, Zhifeng Zhao, Xianfu Chen, Jacques Palicot, and Honggang Zhang · 2014
Earlier work this paper cites.
Inferring origin flow patterns in wi-fi with deep learning
Youngjune L Gwon and HT Kung · 2014
Earlier work this paper cites.
Convolutional neural networks for human activity recognition using mobile sensors
Ming Zeng, Le T Nguyen, Bo Yu, Ole J Mengshoel, Jiang Zhu, Pang Wu, and Joy Zhang · 2014
Earlier work this paper cites.
Implementation of artificial neural network for mobile movement prediction
J Venkata Subramanian and M Abdul Karim Sadiq · 2014
Earlier work this paper cites.
Effective neural network-based node localisation scheme for wireless sensor networks
Po-Jen Chuang and Yi-Jun Jiang · 2014
Earlier work this paper cites.
Droid-Sec: deep learning in Android malware detection
Zhenlong Yuan, Yongqiang Lu, Zhaoguo Wang, and Yibo Xue · 2014
Earlier work this paper cites.
Mobile big data analytics: research, practice, and opportunities
Demetrios Zeinalipour Yazti and Shonali Krishnaswamy · 2014
Earlier work this paper cites.
Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis
Heiga Zen and Andrew Senior · 2014
Earlier work this paper cites.
Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
Earlier work this paper cites.
Backhauling 5G small cells: A radio resource management perspective
Ning Wang, Ekram Hossain, and Vijay K Bhargava · 2015
Earlier work this paper cites.
Distributed mobility management for future 5G networks: overview and analysis of existing approaches
Fabio Giust, Luca Cominardi, and Carlos J Bernardos · 2015
Earlier work this paper cites.
A survey of 5G network: Architecture and emerging technologies
Akhil Gupta and Rakesh Kumar Jha · 2015
Earlier work this paper cites.
A convolutional neural network for leaves recognition using data augmentation
Chaoyun Zhang, Pan Zhou, Chenghua Li, and Lijun Liu · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
Earlier work this paper cites.
Deep learning applications and challenges in big data analytics
Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall Wald, and Edin Muharemagic · 2015
Earlier work this paper cites.
System architecture and key technologies for 5G heterogeneous cloud radio access networks
Mugen Peng, Yong Li, Zhongyuan Zhao, and Chonggang Wang · 2015
Earlier work this paper cites.
A survey of millimeter wave communications (mmwave) for 5G: opportunities and challenges
Yong Niu, Yong Li, Depeng Jin, Li Su, and Athanasios V Vasilakos · 2015
Earlier work this paper cites.
Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world
Qilong Han, Shuang Liang, and Hongli Zhang · 2015
Earlier work this paper cites.
Energy-efficiency oriented traffic offloading in wireless networks: A brief survey and a learning approach for heterogeneous cellular networks
Xianfu Chen, Jinsong Wu, Yueming Cai, Honggang Zhang, and Tao Chen · 2015
Earlier work this paper cites.
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence
Michele Zorzi, Andrea Zanella, Alberto Testolin, Michele De Filippo De Grazia, and Marco Zorzi · 2015
Earlier work this paper cites.
Can deep learning revolutionize mobile sensing?
Nicholas D Lane and Petko Georgiev · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Petuum: A new platform for distributed machine learning on big data
Eric P Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, and Yaoliang Yu · 2015
Earlier work this paper cites.
Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Convolutional LSTM network: A machine learning approach for precipitation nowcasting
Shi Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
Earlier work this paper cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Application of reinforcement learning to routing in distributed wireless networks: a review
Hasan AA Al-Rawi, Ming Ann Ng, and Kok-Lim Alvin Yau · 2015
Earlier work this paper cites.
Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems
Yan-Jun Liu, Li Tang, Shaocheng Tong, CL Philip Chen, and Dong-Juan Li · 2015
Earlier work this paper cites.
The generalization ability of artificial neural networks in forecasting TCP/IP traffic trends: How much does the size of learning rate matter?
Vusumuzi Moyo et al · 2015
Earlier work this paper cites.
The applications of deep learning on traffic identification
Zhanyi Wang · 2015
Earlier work this paper cites.
Using distance estimation and deep learning to simplify calibration in food calorie measurement
Pallavi Kuhad, Abdulsalam Yassine, and Shervin Shimohammadi · 2015
Earlier work this paper cites.
Using deep learning for energy expenditure estimation with wearable sensors
Jindan Zhu, Amit Pande, Prasant Mohapatra, and Jay J Han · 2015
Earlier work this paper cites.
A deep learning approach to human activity recognition based on single accelerometer
Yuqing Chen and Yang Xue · 2015
Earlier work this paper cites.
PhaseFi: Phase fingerprinting for indoor localization with a deep learning approach
Xuyu Wang, Lingjun Gao, and Shiwen Mao · 2015
Earlier work this paper cites.
The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices
Takuya Yoshioka, Nobutaka Ito, Marc Delcroix, Atsunori Ogawa, Keisuke Kinoshita, Masakiyo Fujimoto, Chengzhu Yu, Wojciech J Fabian, Miquel Espi, Takuya Higuchi, et al · 2015
Earlier work this paper cites.
DeepFi: Deep learning for indoor fingerprinting using channel state information
Xuyu Wang, Lingjun Gao, Shiwen Mao, and Santosh Pandey · 2015
Earlier work this paper cites.
Fully connected neural networks ensemble with signal strength clustering for indoor localization in wireless sensor networks
Marcin Bernas and Bartłomiej Płaczek · 2015
Earlier work this paper cites.
Analysis of some feedforward artificial neural network training algorithms for developing localization framework in wireless sensor networks
Ashish Payal, Chandra Shekhar Rai, and BV Ramana Reddy · 2015
Earlier work this paper cites.
Distributed data mining based on deep neural network for wireless sensor network
Chunlin Li, Xiaofu Xie, Yuejiang Huang, Hong Wang, and Changxi Niu · 2015
Earlier work this paper cites.
Intercell-interference cancellation and neural network transmit power optimization for MIMO channels
Michael Andri Wijaya, Kazuhiko Fukawa, and Hiroshi Suzuki · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Earlier work this paper cites.
Internet of things: A survey on enabling technologies, protocols, and applications
Ala Al-Fuqaha, Mohsen Guizani, Mehdi Mohammadi, Mohammed Aledhari, and Moussa Ayyash · 2015
Earlier work this paper cites.
An early resource characterization of deep learning on wearables, smartphones and internet-of-things devices
Nicholas D Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, and Fahim Kawsar · 2015
Earlier work this paper cites.
A transfer learning approach for cache-enabled wireless networks
Ejder Baştuğ, Mehdi Bennis, and Mérouane Debbah · 2015
Earlier work this paper cites.
Understanding mobile traffic patterns of large scale cellular towers in urban environment
Huandong Wang, Fengli Xu, Yong Li, Pengyu Zhang, and Depeng Jin · 2015
Earlier work this paper cites.
A multi-source dataset of urban life in the city of Milan and the province of Trentino
Gianni Barlacchi, Marco De Nadai, Roberto Larcher, Antonio Casella, Cristiana Chitic, Giovanni Torrisi, Fabrizio Antonelli, Alessandro Vespignani, Alex Pentland, and Bruno Lepri · 2015
Earlier work this paper cites.
Urban resolution: New metric for measuring the quality of urban sensing
Liang Liu, Wangyang Wei, Dong Zhao, and Huadong Ma · 2015
Earlier work this paper cites.
Next generation 5G wireless networks: A comprehensive survey
Mamta Agiwal, Abhishek Roy, and Navrati Saxena · 2016
Earlier work this paper cites.
Big data-driven optimization for mobile networks toward 5G
Kan Zheng, Zhe Yang, Kuan Zhang, Periklis Chatzimisios, Kan Yang, and Wei Xiang · 2016
Earlier work this paper cites.
Evaluation of machine learning classifiers for mobile malware detection
Fairuz Amalina Narudin, Ali Feizollah, Nor Badrul Anuar, and Abdullah Gani · 2016
Earlier work this paper cites.
Mobile big data analytics using deep learning and Apache Spark
Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, and Zhu Han · 2016
Earlier work this paper cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
A survey on 5G: The next generation of mobile communication
Nisha Panwar, Shantanu Sharma, and Awadhesh Kumar Singh · 2016
Earlier work this paper cites.
A comprehensive survey of pilot contamination in massive MIMO–5G system
Olakunle Elijah, Chee Yen Leow, Tharek Abdul Rahman, Solomon Nunoo, and Solomon Zakwoi Iliya · 2016
Earlier work this paper cites.
A survey of energy-efficient techniques for 5G networks and challenges ahead
Stefano Buzzi, I Chih-Lin, Thierry E Klein, H Vincent Poor, Chenyang Yang, and Alessio Zappone · 2016
Earlier work this paper cites.
Big data meet green challenges: big data toward green applications
Jinsong Wu, Song Guo, Jie Li, and Deze Zeng · 2016
Earlier work this paper cites.
Can machine learning aid in delivering new use cases and scenarios in 5G?
Teodora Sandra Buda, Haytham Assem, Lei Xu, Danny Raz, Udi Margolin, Elisha Rosensweig, Diego R Lopez, Marius-Iulian Corici, Mikhail Smirnov, Robert Mullins, et al · 2016
Earlier work this paper cites.
Conceptual design of proactive SONs based on the big data framework for 5G cellular networks: A novel machine learning perspective facilitating a shift in the son paradigm
Bharath Keshavamurthy and Mohammad Ashraf · 2016
Earlier work this paper cites.
Neural networks in wireless networks: Techniques, applications and guidelines
Nauman Ahad, Junaid Qadir, and Nasir Ahsan · 2016
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
One-shot generalization in deep generative models
Danilo Rezende, Ivo Danihelka, Karol Gregor, Daan Wierstra, et al · 2016
Earlier work this paper cites.
Deep neural networks for wireless localization in indoor and outdoor environments
Wei Zhang, Kan Liu, Weidong Zhang, Youmei Zhang, and Jason Gu · 2016
Earlier work this paper cites.
Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition
Francisco Javier Ordóñez and Daniel Roggen · 2016
Earlier work this paper cites.
Distributed neural networks for Internet of Things: the big-little approach
Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Steven Bohez, Pieter Simoens, Piet Demeester, and Bart Dhoedt · 2016
Earlier work this paper cites.
TensorFlow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Earlier work this paper cites.
Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
Earlier work this paper cites.
Geeps: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server
Henggang Cui, Hao Zhang, Gregory R Ganger, Phillip B Gibbons, and Eric P Xing · 2016
Earlier work this paper cites.
An overview of gradient descent optimization algorithms
Sebastian Ruder · 2016
Earlier work this paper cites.
Incorporating Nesterov momentum into Adam
Timothy Dozat · 2016
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, and Nando de Freitas · 2016
Earlier work this paper cites.
Design and tool flow of IBM’s truenorth: an ultra-low power programmable neurosynaptic chip with 1 million neurons
Filipp Akopyan · 2016
Earlier work this paper cites.
Cnndroid: GPU-accelerated execution of trained deep convolutional neural networks on Android
Seyyed Salar Latifi Oskouei, Hossein Golestani, Matin Hashemi, and Soheil Ghiasi · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
A hybrid autoencoder and density estimation model for anomaly detection
Miguel Nicolau, James McDermott, et al · 2016
Earlier work this paper cites.
Towards multimodal deep learning for activity recognition on mobile devices
Valentin Radu, Nicholas D Lane, Sourav Bhattacharya, Cecilia Mascolo, Mahesh K Marina, and Fahim Kawsar · 2016
Earlier work this paper cites.
Learning deeply coupled autoencoders for smartphone based robust periocular verification
Ramachandra Raghavendra and Christoph Busch · 2016
Earlier work this paper cites.
Wavelet-based stacked denoising autoencoders for cell phone base station user number prediction
Jing Li, Jingyuan Wang, and Zhang Xiong · 2016
Earlier work this paper cites.
Supervised and semi-supervised text categorization using LSTM for region embeddings
Rie Johnson and Tong Zhang · 2016
Earlier work this paper cites.
NIPS 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
Earlier work this paper cites.
Continuous deep Q-learning with model-based acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
Earlier work this paper cites.
A neural network for quality of experience estimation in mobile communications
Laura Pierucci and Davide Micheli · 2016
Earlier work this paper cites.
Mercury: Metro density prediction with recurrent neural network on streaming CDR data
Victor C Liang, Richard TB Ma, Wee Siong Ng, Li Wang, Marianne Winslett, Huayu Wu, Shanshan Ying, and Zhenjie Zhang · 2016
Earlier work this paper cites.
Using deep learning to predict demographics from mobile phone metadata
Bjarke Felbo, Pål Sundsøy, Alex’Sandy’ Pentland, Sune Lehmann, and Yves-Alexandre de Montjoye · 2016
Earlier work this paper cites.
Poster: Mobiear-building an environment-independent acoustic sensing platform for the deaf using deep learning
Sicong Liu and Junzhao Du · 2016
Earlier work this paper cites.
Integrating mobile and cloud for PPG signal selection to monitor heart rate during intensive physical exercise
Vasu Jindal · 2016
Earlier work this paper cites.
A deep semantic mobile application for thyroid cytopathology
Edward Kim, Miguel Corte-Real, and Zubair Baloch · 2016
Earlier work this paper cites.
Sleep quality prediction from wearable data using deep learning
Aarti Sathyanarayana, Shafiq Joty, Luis Fernandez-Luque, Ferda Ofli, Jaideep Srivastava, Ahmed Elmagarmid, Teresa Arora, and Shahrad Taheri · 2016
Earlier work this paper cites.
Deepcham: Collaborative edge-mediated adaptive deep learning for mobile object recognition
Dawei Li, Theodoros Salonidis, Nirmit V Desai, and Mooi Choo Chuah · 2016
Earlier work this paper cites.
Convolutional neural networks for object recognition on mobile devices: A case study
Luis Tobías, Aurélien Ducournau, François Rousseau, Grégoire Mercier, and Ronan Fablet · 2016
Earlier work this paper cites.
DeepFoodCam: A DCNN-based real-time mobile food recognition system
Ryosuke Tanno, Koichi Okamoto, and Keiji Yanai · 2016
Earlier work this paper cites.
Facial expressions recognition based on convolutional neural networks for mobile virtual reality
Teng Teng and Xubo Yang · 2016
Earlier work this paper cites.
Deep learning for RFID-based activity recognition
Xinyu Li, Yanyi Zhang, Ivan Marsic, Aleksandra Sarcevic, and Randall S Burd · 2016
Earlier work this paper cites.
From smart to deep: Robust activity recognition on smartwatches using deep learning
Sourav Bhattacharya and Nicholas D Lane · 2016
Earlier work this paper cites.
A general purpose intelligent surveillance system for mobile devices using deep learning
Antreas Antoniou and Plamen Angelov · 2016
Earlier work this paper cites.
Interacting with Soli: Exploring fine-grained dynamic gesture recognition in the radio-frequency spectrum
Saiwen Wang, Jie Song, Jaime Lien, Ivan Poupyrev, and Otmar Hilliges · 2016
Earlier work this paper cites.
ihear food: Eating detection using commodity bluetooth headsets
Yang Gao, Ning Zhang, Honghao Wang, Xiang Ding, Xu Ye, Guanling Chen, and Yu Cao · 2016
Earlier work this paper cites.
Deep learning applied to mobile phone data for individual income classification
Pål Sundsøy, Johannes Bjelland, B Reme, A Iqbal, and Eaman Jahani · 2016
Earlier work this paper cites.
Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors
Sojeong Ha and Seungjin Choi · 2016
Earlier work this paper cites.
Binarized-BLSTM-RNN based human activity recognition
Marcus Edel and Enrico Köppe · 2016
Earlier work this paper cites.
Spotgarbage: smartphone app to detect garbage using deep learning
Gaurav Mittal, Kaushal B Yagnik, Mohit Garg, and Narayanan C Krishnan · 2016
Earlier work this paper cites.
CSI phase fingerprinting for indoor localization with a deep learning approach
Xuyu Wang, Lingjun Gao, and Shiwen Mao · 2016
Earlier work this paper cites.
Personalized speech recognition on mobile devices
Ian McGraw, Rohit Prabhavalkar, Raziel Alvarez, Montse Gonzalez Arenas, Kanishka Rao, David Rybach, Ouais Alsharif, Haşim Sak, Alexander Gruenstein, Françoise Beaufays, et al · 2016
Earlier work this paper cites.
On the compression of recurrent neural networks with an application to LVCSR acoustic modeling for embedded speech recognition
Rohit Prabhavalkar, Ouais Alsharif, Antoine Bruguier, and Lan McGraw · 2016
Earlier work this paper cites.
Speech is 3x faster than typing for english and mandarin text entry on mobile devices
Sherry Ruan, Jacob O Wobbrock, Kenny Liou, Andrew Ng, and James Landay · 2016
Earlier work this paper cites.
Reducing distraction of smartwatch users with deep learning
Jemin Lee, Jinse Kwon, and Hyungshin Kim · 2016
Earlier work this paper cites.
Transportation mode detection on mobile devices using recurrent nets
Toan H Vu, Le Dung, and Jia-Ching Wang · 2016
Earlier work this paper cites.
Xi Ouyang, Chaoyun Zhang, Pan Zhou, and Hao Jiang · 2016
Earlier work this paper cites.
DeepTransport: Prediction and simulation of human mobility and transportation mode at a citywide level
Xuan Song, Hiroshi Kanasugi, and Ryosuke Shibasaki · 2016
Earlier work this paper cites.
Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference
Quanjun Chen, Xuan Song, Harutoshi Yamada, and Ryosuke Shibasaki · 2016
Earlier work this paper cites.
Neural turing machine for sequential learning of human mobility patterns
Jan Tkačík and Pavel Kordík · 2016
Earlier work this paper cites.
Device-free wireless localization and activity recognition with deep learning
Xiao Zhang, Jie Wang, Qinghua Gao, Xiaorui Ma, and Hongyu Wang · 2016
Earlier work this paper cites.
Mobile device based outdoor navigation with on-line learning neural network: A comparison with convolutional neural network
Zejia Zhengj and Juyang Weng · 2016
Earlier work this paper cites.
Real-time identification of smoldering and flaming combustion phases in forest using a wireless sensor network-based multi-sensor system and artificial neural network
Xiaofei Yan, Hong Cheng, Yandong Zhao, Wenhua Yu, Huan Huang, and Xiaoliang Zheng · 2016
Earlier work this paper cites.
Adaptive and intelligent wireless sensor networks through neural networks: an illustration for infrastructure adaptation through hopfield network
Jiakai Li and Gursel Serpen · 2016
Earlier work this paper cites.
Rate-distortion balanced data compression for wireless sensor networks
Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, and Hwee-Pink Tan · 2016
Earlier work this paper cites.
Robust ANNs-Based WSN Localization in the Presence of Anisotropic Signal Attenuation
Ahmad El Assaf, Slim Zaidi, Sofiène Affes, and Nahi Kandil · 2016
Earlier work this paper cites.
Poster: Deep learning enabled M2M gateway for network optimization
Shivashankar Subramanian and Arindam Banerjee · 2016
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
Multi-objective reinforcement learning for cognitive radio–based satellite communications
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
Autoencoder-based feature learning for cyber security applications
Mahmood Yousefi-Azar, Vijay Varadharajan, Len Hamey, and Uday Tupakula · 2017
Later among the works it cites.
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Later among the works it cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
AMP-inspired deep networks for sparse linear inverse problems
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
A neural network approach to jointly modeling social networks and mobile trajectories
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Later among the works it cites.
Indoor fingerprint positioning based on Wi-Fi: An overview
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Later among the works it cites.
A survey of selected indoor positioning methods for smartphones
Pavel Davidson and Robert Piché · 2017
Later among the works it cites.
Deep learning control for complex and large scale cloud systems
Mehdi Roopaei, Paul Rad, and Mo Jamshidi · 2017
Later among the works it cites.
A survey of deep learning-based network anomaly detection
Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C Suh, Ikkyun Kim, and Kuinam J Kim · 2017
Later among the works it cites.
The evolution of android malware and Android analysis techniques
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Later among the works it cites.
PassGAN: A deep learning approach for password guessing
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Later among the works it cites.
Net2Vec: Deep learning for the network
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Later among the works it cites.
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Nicholas D Lane, Sourav Bhattacharya, Akhil Mathur, Petko Georgiev, Claudio Forlivesi, and Fahim Kawsar · 2017
Later among the works it cites.
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Jie Tang, Dawei Sun, Shaoshan Liu, and Jean-Luc Gaudiot · 2017
Later among the works it cites.
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Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2017
Later among the works it cites.
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Later among the works it cites.
A tucker deep computation model for mobile multimedia feature learning
Qingchen Zhang, Laurence T Yang, Xingang Liu, Zhikui Chen, and Peng Li · 2017
Later among the works it cites.
MobiRNN: Efficient recurrent neural network execution on mobile GPU
Qingqing Cao, Niranjan Balasubramanian, and Aruna Balasubramanian · 2017
Later among the works it cites.
DeepMon: Building mobile GPU deep learning models for continuous vision applications
Loc N Huynh, Rajesh Krishna Balan, and Youngki Lee · 2017
Later among the works it cites.
MEC: Memory-efficient convolution for deep neural network
Minsik Cho and Daniel Brand · 2017
Later among the works it cites.
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Jia Guo and Miodrag Potkonjak · 2017
Later among the works it cites.
Fitcnn: A cloud-assisted lightweight convolutional neural network framework for mobile devices
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
Deep decentralized multi-task multi-agent reinforcement learning under partial observability
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
Practical secure aggregation for privacy preserving machine learning
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Later among the works it cites.
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B McMahan and Daniel Ramage · 2017
Later among the works it cites.
Joint spatial and temporal classification of mobile traffic demands
Angelo Fumo, Marco Fiore, and Razvan Stanica · 2017
Later among the works it cites.
Lifelong learning of human actions with deep neural network self-organization
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Later among the works it cites.
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Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J Mankowitz, and Shie Mannor · 2017
Later among the works it cites.
Zero-shot task generalization with multi-task deep reinforcement learning
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
Deep learning advances in computer vision with 3D data: A survey
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
Anomaly detection with robust deep autoencoders
Chong Zhou and Randy C Paffenroth · 2017
Later among the works it cites.
Learning to protect communications with adversarial neural cryptography
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Later among the works it cites.
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Closest in time.
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