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Deep Neural Mobile Networking

Deep Neural Mobile Networking

23 October 2020
Chaoyun Zhang
ArXivPDFHTML

Papers citing "Deep Neural Mobile Networking"

8 / 8 papers shown
Title
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
27
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
48
114
0
26 Jul 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
226
1,835
0
03 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
236
1,811
0
25 Nov 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
252
5,833
0
08 Jul 2016
Recurrent Neural Networks for Multivariate Time Series with Missing
  Values
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Zhengping Che
S. Purushotham
Kyunghyun Cho
David Sontag
Yan Liu
AI4TS
205
1,895
0
06 Jun 2016
Effective Approaches to Attention-based Neural Machine Translation
Effective Approaches to Attention-based Neural Machine Translation
Thang Luong
Hieu H. Pham
Christopher D. Manning
214
7,923
0
17 Aug 2015
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
224
7,902
0
13 Jun 2015
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