Deep Learning in Mobile and Wireless Networking: A Survey
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brock, 2018, Large scale GAN training for high fidelity natural image synthesis, arXiv preprint arXiv 1809 11096
hessel, 2017, Rainbow: Combining improvements in deep reinforcement learning, Proc AAAI Conf Artif Intell (AAAI), 3215
silver, 2016, Mastering the game of GO with deep neural networks and tree search, Nature, 529, 484, 10.1038/nature16961
schulman, 2017, Proximal policy optimization algorithms, arXiv preprint arXiv 1707 08386
klambauer, 2017, Self-normalizing neural networks, Proc Adv Neural Inf Process Syst, 971
glorot, 2011, Deep sparse rectifier neural networks, Proc 14th Int Conf Artificial Intell, 315
ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proc Int Conf Mach Learn, 448
xingjian, 2015, Convolutional LSTM network: A machine learning approach for precipitation nowcasting, Proc Adv Neural Inf Process Syst, 802
qi, 2018, Loss-sensitive generative adversarial networks on Lipschitz densities, IEEE Trans Pattern Anal Mach Intell, 1
dauphin, 2013, Stochastic ratio matching of RBMs for sparse high-dimensional inputs, Proc Adv Neural Inf Process Syst, 1340
ruan, 2016, Speech is 3x faster than typing for English and mandarin text entry on mobile devices, arXiv preprint arXiv 1608 07323
2017, Deep Learning for Siri’s Voice On-Device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis
ouyang, 2016, DeepSpace: An online deep learning framework for mobile big data to understand human mobility patterns, arXiv preprint arXiv 1610 09756
zhang, 2017, Deep spatio-temporal residual networks for citywide crowd flows prediction, Proc Nat Conf Artif Intell (AAAI), 1655
song, 2016, DeepTransport: Prediction and simulation of human mobility and transportation mode at a citywide level, Proc Int Joint Conf Artif Intell, 2618
subramanian, 2014, Implementation of artificial neural network for mobile movement prediction, Indian J of Sci and Technol, 7, 858, 10.17485/ijst/2014/v7i6.12
akopyan, 2016, Design and tool flow of IBM’s TrueNorth: An ultra-low power programmable neurosynaptic chip with 1 million neurons, Proc ACM Int Symp Phys Design, 59, 10.1145/2872334.2878629
bang, 2017, 14.7 A $288~\mu\text{W}$ programmable deep-learning processor with 270KB on-chip weight storage using non-uniform memory hierarchy for mobile intelligence, Proc IEEE Int Conf Solid-State Circuits (ISSCC), 250
zhou, 2018, Stable gradient descent, Proc Conf Uncertainty Artif Intell, 1
wen, 2017, TernGrad: Ternary gradients to reduce communication in distributed deep learning, Proc Adv Neural Inf Process Syst, 1
sutskever, 2014, Sequence to sequence learning with neural networks, Proc Adv Neural Inf Process Syst, 3104
kingma, 2014, Auto-encoding variational Bayes, Proc of the Int Conf on Learning Representations (ICLR)
vincent, 2010, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J Mach Learn Res, 11, 3371
oskouei, 2016, CNNdroid: GPU-accelerated execution of trained deep convolutional neural networks on Android, Proc ACM Multimedia Conf, 1201, 10.1145/2964284.2973801
cortes, 2017, AdaNet: Adaptive structural learning of artificial neural networks, Proc Int Conf Mach Learn (ICML), 874
johnson, 2016, Supervised and semi-supervised text categorization using LSTM for region embeddings, Proc Int Conf Mach Learn (ICML), 526
goodfellow, 2016, NIPS 2016 tutorial: Generative adversarial networks, arXiv preprint arXiv 1701 00295
zhu, 2018, Deformable ConvNets v2: More deformable, better results, arXiv preprint arXiv 1811 11168
cho, 2017, MEC: Memory-efficient convolution for deep neural network, Proc Int Conf Mach Learn (ICML), 815
rallapalli, 0, Are very deep neural networks feasible on mobile devices, IEEE Trans Circuits Syst Video Technol
li, 2018, DeepRebirth: Accelerating deep neural network execution on mobile devices, Proc Nat Conf Artif Intell (AAAI), 2322
omidshafiei, 2017, Deep decentralized multi-task multi-agent reinforcement learning under partial observability, Proc Int Conf Mach Learn (ICML), 2681
chen, 2018, TVM: An automated end-to-end optimizing compiler for deep learning, Proc of USENIX Symp on Operating Systems Design and Implementation (OSDI), 578
recht, 2011, Hogwild: A lock-free approach to parallelizing stochastic gradient descent, Proc Adv Neural Inf Process Syst, 693
zhang, 2016, Asynchronous distributed semi-stochastic gradient optimization, Proc Nat Conf Artif Intell (AAAI), 2323
goyal, 2017, Accurate, large minibatch SGD: Training ImageNet in 1 hour, arXiv preprint arXiv 1706 02677
qi, 2017, PointNet++: Deep hierarchical feature learning on point sets in a metric space, Proc Adv Neural Inf Process Syst, 5099
roux, 2004, Geometric Data Analysis From Correspondence Analysis to Structured Data Analysis
aminanto, 2016, Detecting impersonation attack in WiFi networks using deep learning approach, Proc Int Workshop Inf Security Appl, 136
feng, 2016, Anomaly detection of spectrum in wireless communication via deep autoencoder, Proc Int Conf Comput Sci Appl, 259
gaw?owicz, 2018, ns3-gym: Extending OpenAI Gym for networking research, arXiv preprint arXiv 1810 06008
gu, 2016, Continuous deep Q-learning with model-based acceleration, Proc Int Conf Mach Learn, 2829
hitaj, 2017, PassGAN: A deep learning approach for password guessing, arXiv preprint arXiv 1709 00440
pham, 2018, Deep reinforcement learning based QoS-aware routing in knowledge-defined networking, Proc Qshine EAI Int Conf Heterogeneous Netw Qual Rel Security Robustness, 1
2019, Alphastar Mastering the real-time strategy game StarCraft II
iandola, 2017, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size, Proc Int Conf Learn Represent
howard, 2017, MobileNets: Efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv 1704 04861
gwon, 2014, Inferring origin flow patterns in Wi-Fi with deep learning, Proc 11th IEEE Int Conf Auton Comput (ICAC)), 73
wang, 2015, The applications of deep learning on traffic identification, Proc BlackHat USA, 21
yin, 0, Deep generative models of urban mobility, IEEE Trans Intell Transp Syst
felbo, 2016, Using deep learning to predict demographics from mobile phone metadata, Proc Int Conf Learni Represent Workshop Track
lotfollahi, 2017, Deep packet: A novel approach for encrypted traffic classification using deep learning, arXiv preprint arXiv 1709 04396
dean, 2012, Large scale distributed deep networks, Proc Adv Neural Inf Process Syst, 1223
sutskever, 2013, On the importance of initialization and momentum in deep learning, Proc Int Conf Mach Learn (ICML), 28, 1139
2017, Core ml Integrate machine learning models into your app
2017, Huawei Announces the Kirin 970–New Flagship SoC With AI Capabilities
kraska, 2013, $MLbase$ : A distributed machine-learning system, Proc CIDR, 1, 1
kingma, 2015, Adam: A method for stochastic optimization, Proc of the Int Conf on Learning Representations (ICLR), 1
chilimbi, 2014, Project ADAM: Building an efficient and scalable deep learning training system, Proc of the 2nd USENIX Symp on Operating Systems Design and Implementation (OSDI), 14, 571
mirhoseini, 2017, Device placement optimization with reinforcement learning, Proc Int Conf Mach Learn, 1
moritz, 2018, Ray: A distributed framework for emerging AI applications, Proc of USENIX Symp on Operating Systems Design and Implementation (OSDI), 561
paszke, 2017, On Automatic Differentiation
chen, 2015, MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems, arXiv preprint arXiv 1512 00327
ruder, 2016, An overview of gradient descent optimization algorithms, arXiv preprint arXiv 1609 09861
zeiler, 2012, ADADELTA: An adaptive learning rate method, arXiv preprint arXiv 1212 5701
dozat, 2016, Incorporating Nesterov momentum into Adam, Proc Workshop Track (ICLR), 1
andrychowicz, 2016, Learning to learn by gradient descent by gradient descent, Proc Adv Neural Inf Process Syst, 3981
2017, Cisco Visual Networking Index Forecast and Methodology 2016–2021
sathyanarayana, 2016, Sleep quality prediction from wearable data using deep learning, JMIR Mhealth Uhealth, 4
stamate, 2017, Deep learning Parkinson’s from smartphone data, Proc IEEE Int Conf Pervasive Comput Commun (PerCom), 31
kim, 2016, A deep semantic mobile application for thyroid cytopathology, Proc Med Imag PACS Imag Informat Next Gener Innov, 9789
wu, 2018, Beyond sparsity: Tree regularization of deep models for interpretability, Proc AAAI Conf Artif Intell (AAAI), 1670
behzadan, 2017, Vulnerability of deep reinforcement learning to policy induction attacks, Proc Mach Learning Data Min Pattern Recognition, 262, 10.1007/978-3-319-62416-7_19
kipf, 2017, Semi-supervised classification with graph convolutional networks, Proc of the Int Conf on Learning Representations (ICLR), 1
perez, 2017, The effectiveness of data augmentation in image classification using deep learning, arXiv preprint arXiv 1712 04621
almaslukh, 2017, An effective deep autoencoder approach for online smartphone-based human activity recognition, Int J Comput Sci Netw Security, 17, 160
hsieh, 2017, Gaia: Geo-distributed machine learning approaching LAN speeds, Proc 1st USENIX Symp Netw Syst Design Implement (NSDI '04), 629
xiao, 2017, Tux2: Distributed graph computation for machine learning, Proc 1st USENIX Symp Netw Syst Design Implement (NSDI '04), 669
paolini, 2017, Mastering Analytics How to Benefit From Big Data and Network Complexity An Analyst Report
socher, 2012, Deep learning for NLP (without magic), Proc Tuts Abstracts ACL, 5
2017, IEEE Network Exploring Deep Learning for Efficient and Reliable Mobile Sensing
abadi, 2016, TensorFlow: A system for large-scale machine learning, Proc of the 2nd USENIX Symp on Operating Systems Design and Implementation (OSDI), 16, 265
chetlur, 2014, cuDNN: Efficient primitives for deep learning, arXiv preprint arXiv 1410 0759
goodfellow, 2016, Deep Learning
2016, Theano: A Python framework for fast computation of mathematical expressions, arXiv e prints abs/1605 02688
mnih, 2015, Human-level control through deep reinforcement learning, Nature, 518, 529, 10.1038/nature14236
collobert, 2011, Torch7: A MATLAB-like environment for machine learning, NIPS Workshop BigLearn, 1
2017, NCNN—A High-Performance Neural Network Inference Framework Optimized for the Mobile Platform
vinyals, 2016, Matching networks for one shot learning, Proc Adv Neural Inf Process Syst, 3630
mismar, 2017, Deep reinforcement learning for improving downlink mmWave communication performance, arXiv preprint arXiv 1707 00408
palatucci, 2009, Zero-shot learning with semantic output codes, Proc Adv Neural Inf Process Syst, 1410
ba?tu?, 2015, A transfer learning approach for cache-enabled wireless networks, Proc Int Symp Model Optim Mobile Ad-Hoc Wireless Netw (WiOpt), 161
tessler, 2017, A deep hierarchical approach to lifelong learning in minecraft, Proc Nat Conf Artif Intell (AAAI), 1553
barlacchi, 2015, A multi-source dataset of urban life in the city of Milan and the Province of Trentino, Data Science Journal, 2
oh, 2017, Zero-shot task generalization with multi-task deep reinforcement learning, Proc Int Conf Mach Learn (ICML), 2661
hordri, 2017, A systematic literature review on features of deep learning in big data analytics, Int J Adv Soft Comput Appl, 9, 32
bonawitz, 2019, Towards federated learning at scale: System design, arXiv preprint arXiv 1902 05023
mcmahan, 2017, Federated learning Collaborative machine learning without centralized training data
fumo, 2017, Joint spatial and temporal classification of mobile traffic demands, Proc IEEE Conf Comput Commun, 1
mcmahan, 2017, Communication-efficient learning of deep networks from decentralized data, Proc Int Conf Artif Intell Statist, 54, 1273
ho, 2016, Generative adversarial imitation learning, Proc Adv Neural Inf Process Syst, 4565
ferreira, 2018, Multiobjective reinforcement learning for cognitive satellite communications using deep neural network ensembles, IEEE J Sel Areas Commun, 36, 1030, 10.1109/JSAC.2018.2832820
schaul, 2016, Prioritized experience replay, Proc Int Conf Learn Represent
lee, 2016, Dual-memory deep learning architectures for lifelong learning of everyday human behaviors, Proc Int Joint Conf Artif Intell, 1669
heydari, 2017, Reduce energy consumption and send secure data wireless multimedia sensor networks using a combination of techniques for multi-layer watermark and deep learning, Int J Comput Sci Netw Security, 17, 98
graves, 2016, Hybrid computing using a neural network with dynamic external memory, Nature, 538, 471, 10.1038/nature20101
oda, 2017, Performance evaluation of a deep Q-network based simulation system for actor node mobility control in wireless sensor and actor networks considering three-dimensional environment, Proc Int Conf Int Netw Collaborative Syst, 41
graves, 2014, Neural Turing machines, arXiv preprint arXiv 1410 5401
vyas, 2017, A survey on human activity recognition using smartphone, Int J Adv Res Comput Sci Manag Stud, 5, 118
o’shea, 2016, Deep reinforcement learning radio control and signal detection with KeRLym, a Gym RL agent, arXiv preprint arXiv 1605 09221
chinchali, 2018, Cellular network traffic scheduling with deep reinforcement learning, Proc Nat Conf Artif Intell (AAAI)
ngiam, 2011, Multimodal deep learning, Proc 28th Int Conf Mach Learn (ICML), 689
usama, 2017, Unsupervised machine learning for networking: Techniques, applications and research challenges, arXiv preprint arXiv 1709 01922
abadi, 2017, Learning to protect communications with adversarial neural cryptography, Proc Int Conf Learn Represent
chen, 2017, Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks, arXiv preprint arXiv 1710 02913
zhou, 2018, Intelligent wireless communications enabled by cognitive radio and machine learning, China Commun, 15, 16
luong, 2018, Applications of deep reinforcement learning in communications and networking: A survey, arXiv preprint arXiv 1810 06008
li, 2017, Deep reinforcement learning: An overview, arXiv preprint arXiv 1701 07717
wang, 2015, DeepFi: Deep learning for indoor fingerprinting using channel state information, Proc IEEE Wireless Commun Netw Conf (WCNC), 1666
mnih, 2016, Asynchronous methods for deep reinforcement learning, Proc Int Conf Mach Learn (ICML), 1928
wang, 2017, Big data analytics for network intrusion detection: A survey, International Journal of Networks and Communications, 7, 24
tka?ík, 2016, Neural Turing machine for sequential learning of human mobility patterns, Proc IEEE Int Joint Conf Neural Netw (IJCNN), 2790
chen, 2016, Learning deep representation from big and heterogeneous data for traffic accident inference, Proc Nat Conf Artif Intell (AAAI), 338
jiang, 2018, DeepUrbanMomentum: An online deep-learning system for short-term urban mobility prediction, Proc Nat Conf Artif Intell (AAAI), 784
neumann, 2017, Deep channel estimation, Proc International ITG Workshop on Smart Antennas, 1
stewart, 2017, Label-free supervision of neural networks with physics and domain knowledge, Proc Nat Conf Artif Intell (AAAI), 2576
kingma, 2014, Semi-supervised learning with deep generative models, Proc Adv Neural Inf Process Syst, 3581
goodfellow, 2014, Generative adversarial nets, Proc Adv Neural Inf Process Syst, 2672
rasmussen, 2006, Gaussian Processes for Machine Learning, 1
rezende, 2016, One-shot generalization in deep generative models, Proc Int Conf Mach Learn (ICML), 1521
socher, 2013, Zero-shot learning through cross-modal transfer, Proc Adv Neural Inf Process Syst, 935
o’shea, 2017, Deep learning based MIMO communications, arXiv preprint arXiv 1707 07816
greydanus, 2017, Learning the enigma with recurrent neural networks, arXiv preprint arXiv 1708 05227
servia-rodriguez, 2018, Personal model training under privacy constraints, Proc 3rd ACM/IEEE Int Conf Internet Things Design Implement
ossia, 2017, A hybrid deep learning architecture for privacy-preserving mobile analytics, arXiv preprint arXiv 1703 01641
garnelo, 2018, Neural processes, arXiv preprint arXiv 1807 01622
damianou, 2013, Deep Gaussian processes, Proc Artif Intell Stat, 207
arjovsky, 2017, Wasserstein generative adversarial networks, Proc Int Conf Mach Learn, 214
tsang, 2005, Core vector machines: Fast SVM training on very large data sets, J Mach Learn Res, 6, 363
zhengj, 2016, Mobile device based outdoor navigation with on-line learning neural network: A comparison with convolutional neural network, Proc IEEE Conf Comp Vis Pattern Recognit, 11
rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0
lecun, 1995, Convolutional networks for images, speech, and time series, The Handbook of Brain Theory and Neural Networks, 3361
krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Proc Adv Neural Inf Process Syst, 1097
shokri, 2015, Privacy-preserving deep learning, Proc 22nd ACM SIGSAC Conf Comput Commun Security, 1310, 10.1145/2810103.2813687
chen, 2017, Deep learning for secure mobile edge computing, arXiv preprint arXiv 1709 04396
luong, 2018, Joint transaction transmission and channel selection in cognitive radio based blockchain networks: A deep reinforcement learning approach, arXiv preprint arXiv 1810 10053
zhao, 2018, Deep reinforcement learning for network slicing, arXiv preprint arXiv 1805 06591