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Deep Learning in Mobile and Wireless Networking: A Survey

Deep Learning in Mobile and Wireless Networking: A Survey

12 March 2018
Chaoyun Zhang
P. Patras
Hamed Haddadi
ArXivPDFHTML

Papers citing "Deep Learning in Mobile and Wireless Networking: A Survey"

10 / 110 papers shown
Title
Auto-tuning Distributed Stream Processing Systems using Reinforcement
  Learning
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning
Luis M. Vaquero
Félix Cuadrado
OffRL
22
18
0
14 Sep 2018
Deep Learning Classification of 3.5 GHz Band Spectrograms with
  Applications to Spectrum Sensing
Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing
W. Max Lees
Adam Wunderlich
Peter J. Jeavons
P. Hale
M. Souryal
23
87
0
19 Jun 2018
All-Optical Machine Learning Using Diffractive Deep Neural Networks
All-Optical Machine Learning Using Diffractive Deep Neural Networks
Xing Lin
Y. Rivenson
N. Yardimci
Muhammed Veli
Mona Jarrahi
Aydogan Ozcan
76
1,628
0
14 Apr 2018
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a
  Generative Adversarial Neural Network
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network
Chaoyun Zhang
Xi Ouyang
P. Patras
30
68
0
07 Nov 2017
TensorLayer: A Versatile Library for Efficient Deep Learning Development
TensorLayer: A Versatile Library for Efficient Deep Learning Development
Hao Dong
A. Supratak
Luo Mai
Fangde Liu
A. Oehmichen
Simiao Yu
Yike Guo
56
114
0
26 Jul 2017
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio
  Performance Improvement
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement
Faris B. Mismar
B. Evans
38
29
0
10 Jul 2017
An Introduction to Deep Learning for the Physical Layer
An Introduction to Deep Learning for the Physical Layer
Tim O'Shea
J. Hoydis
AI4CE
89
2,171
0
02 Feb 2017
Geometric deep learning on graphs and manifolds using mixture model CNNs
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti
Davide Boscaini
Jonathan Masci
Emanuele Rodolà
Jan Svoboda
M. Bronstein
GNN
251
1,811
0
25 Nov 2016
Google's Neural Machine Translation System: Bridging the Gap between
  Human and Machine Translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu
M. Schuster
Z. Chen
Quoc V. Le
Mohammad Norouzi
...
Alex Rudnick
Oriol Vinyals
G. Corrado
Macduff Hughes
J. Dean
AIMat
716
6,746
0
26 Sep 2016
Convolutional LSTM Network: A Machine Learning Approach for
  Precipitation Nowcasting
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Xingjian Shi
Zhourong Chen
Hao Wang
Dit-Yan Yeung
W. Wong
W. Woo
233
7,904
0
13 Jun 2015
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