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The storage and computation requirements of Convolutional Neural Networks (CNNs) can be prohibitive for exploiting these models over low-power or embedded devices.
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.
An alternating direction method for dual map LP relaxation
Ofer Meshi and Amir Globerson · 2011
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Improving the speed of neural networks on CPUs
Vincent Vanhoucke, Andrew Senior, and Mark Z Mao · 2011
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Distributed robust multicell coordinated beamforming with imperfect CSI: An ADMM approach
Chao Shen, Tsung-Hui Chang, Kun-Yu Wang, Zhengding Qiu, and Chong-Yung Chi · 2012
Earlier work this paper cites.
Speeding up convolutional neural networks with low rank expansions
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
Cited alongside, same era.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
Cited alongside, same era.
Training CNNs with low-rank filters for efficient image classification
Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, and Antonio Criminisi · 2015
Cited alongside, same era.
Sparse convolutional neural networks
Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Pensky · 2015
Cited alongside, same era.
A double-layer ELM with added feature selection ability using a sparse bayesian approach
Farkhondeh Kiaee, Christian Gagné, and Hamid Sheikhzadeh
Cited in the paper.
Relevance vector machine for survival analysis
Farkhondeh Kiaee, Hamid Sheikhzadeh, and Samaneh Eftekhari Mahabadi
Cited in the paper.
Convolutional neural networks with low-rank regularization
Cheng Tai, Tong Xiao, Xiaogang Wang, et al · 2015
Later among the works it cites.
Learning the number of neurons in deep networks
Jose M Alvarez and Mathieu Salzmann · 2016
Closest in time.
Deep compression: Compressing deep neural network with pruning, trained quantization and Huffman coding
Song Han, Huizi Mao, and William J Dally · 2016
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Sparse bayesian mixed-effects extreme learning machine, an approach for unobserved clustered heterogeneity
Farkhondeh Kiaee, Hamid Sheikhzadeh, and Samaneh Eftekhari Mahabadi
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Design of optimal sparse feedback gains via the alternating direction method of multipliers
Fu Lin, Makan Fardad, and Mihailo R Jovanović
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Min Lin, Qiang Chen, and Shuicheng Yan
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