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The holy grail in deep neural network research is porting the memory- and computation-intensive network models on embedded platforms with a minimal compromise in model accuracy.
Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Multi-task feature learning
Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Practical variational inference for neural networks
Alex Graves · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
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Bayesian dark knowledge
Anoop Korattikara Balan, Vivek Rathod, Kevin P Murphy, and Max Welling · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
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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
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Bayesian compression for deep learning
Christos Louizos, Karen Ullrich, and Max Welling · 2017
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Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
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Learning intrinsic sparse structures within long short-term memory
Wei Wen, Yuxiong He, Samyam Rajbhandari, Minjia Zhang, Wenhan Wang, Fang Liu, Bin Hu, Yiran Chen, and Hai Li · 2017
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Model compression and acceleration for deep neural networks: The principles, progress, and challenges
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang · 2018
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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.
Face model compression by distilling knowledge from neurons
Ping Luo, Zhenyao Zhu, Ziwei Liu, Xiaogang Wang, and Xiaoou Tang · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
Cited alongside, same era.
Variational bayesian dropout: pitfalls and fixes
Jiri Hron, Alexander G de G Matthews, and Zoubin Ghahramani · 2018
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Yuhang Liu, Wenyong Dong, Lei Zhang, Dong Gong, and Qinfeng Shi · 2018
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