Fetching the paper…
Reading the bibliography…
Deep convolutional neural networks (CNNs) are powerful tools for a wide range of vision tasks, but the enormous amount of memory and compute resources required by CNNs pose a challenge in deploying them on constrained devices.
Maximum likelihood from incomplete data via the EM algorithm
A. P. Dempster, N. M. Laird, and D. B. Rubin · 1977
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
Geoffrey E. Hinton and Drew van Camp · 1993
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
The CIFAR-10 and CIFAR-100 datasets
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
Earlier work this paper cites.
Sparse convolutional neural networks
Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Penksy · 2015
Earlier work this paper cites.
Dynamic network surgery for efficient DNNs
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
Earlier work this paper cites.
Deep Compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
Song Han, Huizi Mao, and William J Dally · 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.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
Cited alongside, same era.
Convolutional neural networks using logarithmic data representation
Daisuke Miyashita, Edward H. Lee, and Boris Murmann · 2016
Cited alongside, same era.
MobileNets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Towards accurate binary convolutional neural network
Xiaofan Lin, Cong Zhao, and Wei Pan · 2017
Cited alongside, same era.
ThiNet: A filter level pruning method for deep neural network compression
Extremely low bit neural network: Squeeze the last bit out with ADMM
Cong Leng, Zesheng Dou, Hao Li, Shenghuo Zhu, and Rong Jin · 2018
Later among the works it cites.
Model compression via distillation and quantization
Antonio Polino, Razvan Pascanu, and Dan Alistarh · 2018
Later among the works it cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Later among the works it cites.
CLIP-Q: Deep network compression learning by in-parallel pruning-quantization
Frederick Tung, Greg Mori, and Simon Fraser · 2018
Later among the works it cites.
LQ-Nets: Learned quantization for highly accurate and compact deep neural networks
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
Later among the works it cites.
Mayo: A framework for auto-generating hardware friendly deep neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
Cited alongside, same era.
Incremental network quantization: Towards lossless CNNs with low-precision weights
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen · 2017
Cited alongside, same era.
Trained ternary quantization
Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2017
Cited alongside, same era.
Coreset-based neural network compression
Abhimanyu Dubey, Moitreya Chatterjee, and Narendra Ahuja · 2018
Cited alongside, same era.
Yiren Zhao, Xitong Gao, Robert Mullins, and Cheng-Zhong Xu · 2018
Later among the works it cites.
Dynamic channel pruning: Feature boosting and suppression
Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, and Cheng-Zhong Xu · 2019
Closest in time.
Characterizing sources of ineffectual computations in deep learning networks
Milos Nikolic, Mostafa Mahmoud, Andreas Moshovos, Yiren Zhao, and Robert Mullins · 2019
Closest in time.
Automatic generation of multi-precision multi-arithmetic CNN accelerators for FPGAs
Yiren Zhao, Xitong Gao, Xuan Guo, Junyi Liu, Erwei Wang, Robert Mullins, Peter Cheung, George A Constantinides, and Cheng-Zhong Xu · 2019
Closest in time.