Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers

Gradient-based learning applied to document recognition

Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998

Earlier work this paper cites.

Distributed optimization and statistical learning via the alternating direction method of multipliers

Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein · 2011

Earlier work this paper cites.

Imagenet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012

Earlier work this paper cites.

On optimal periodic sensor scheduling for field estimation in wireless sensor networks

Sijia Liu, Makan Fardad, Engin Masazade, and Pramod K. Varshney · 2013

Earlier work this paper cites.

Very deep convolutional networks for large-scale image recognition

Original

Karen Simonyan and Andrew Zisserman · 2014

Earlier work this paper cites.

Learning both weights and connections for efficient neural network

Song Han, Jeff Pool, John Tran, and William Dally · 2015

Cited alongside, same era.

Tensorflow: Large-scale machine learning on heterogeneous distributed systems

Original

Martín Abadi, Ashish Agarwal, Paul Barham, et al · 2016

Cited alongside, same era.

Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding

Song Han, Huizi Mao, and William J Dally · 2016

Cited alongside, same era.

Alternating direction method of multipliers for sparse convolutional neural networks

Original

Farkhondeh Kiaee, Christian Gagné, and Mahdieh Abbasi · 2016

Cited alongside, same era.

Learning structured sparsity in deep neural networks

Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016

Later among the works it cites.

Designing energy-efficient convolutional neural networks using energy-aware pruning

Original

Tien-Ju Yang, Yu-Hsin Chen, and Vivienne Sze · 2016

Later among the works it cites.

Nest: A neural network synthesis tool based on a grow-and-prune paradigm

Original

Xiaoliang Dai, Hongxu Yin, and Niraj K Jha · 2017

Later among the works it cites.

Extremely low bit neural network: Squeeze the last bit out with admm

Original

Cong Leng, Hao Li, Shenghuo Zhu, and Rong Jin · 2017

Later among the works it cites.