Fetching the paper…
Reading the bibliography…
In this paper we apply a compressibility loss that enables learning highly compressible neural network weights.
Optimal brain damage
Yann Lecun, John Denker, and Sara Solla · 1989
Earlier work this paper cites.
Non-negative matrix factorization withsparseness constraints
Patrick O. Hoyer · 2004
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William 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.
Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
Cited alongside, same era.
Fast convnets using group-wise brain damage
Vadim Lebedev and Victor Lempitsky · 2016
Cited alongside, same era.
Less is more: Towards compact cnns
Hao Zhou, Jose M. Alvarez, and Fatih Porikli · 2016
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, and Vincent Vanhoucke · 2017
Cited alongside, same era.
Adaptive mixture of low-rank factorizations for compact neural modeling
Jianping Chen, Ji Lin, Ting chieh Lin, Sunyoung Han, Chong Wang, and Dengyong Zhou · 2018
Cited alongside, same era.
Universal deep neural network compression
Yoojin Choi, Mostafa El-Khamy, and Jungwon Lee · 2018
Later among the works it cites.
Targeted dropout
Aidan Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, and Geoffrey Hinton · 2018
Later among the works it cites.
Smallify: Learning network size while training
Guillaume Leclerc, Manasi Vartak, Raul Castro Fernandez, Tim Kraska, and Samuel Madden · 2018
Later among the works it cites.
https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez_compressed.html , 2019
numpy.savez_compressed · 2019
Closest in time.
Improving the speed of neural networks on cpus
Vincent Vanhoucke and Mark Z. Mao · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…