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Model compression has gained a lot of attention due to its ability to reduce hardware resource requirements significantly while maintaining accuracy of DNNs.
Building a large annotated corpus of English: The Penn Treebank
M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini · 1993
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S. Hochreiter and J. Schmidhuber · 1995
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Low-rank matrix factorization for deep neural network training with high-dimensional output targets
T. N. Sainath, B. Kingsbury, V. Sindhwani, E. Arisoy, and B. Ramabhadran · 2013
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Restructuring of deep neural network acoustic models with singular value decomposition
J. Xue, J. Li, and Y. Gong · 2013
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Learning longer memory in recurrent neural networks
T. Mikolov, A. Joulin, S. Chopra, M. Mathieu, and M. Ranzato · 2014
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Recurrent neural network regularization
W. Zaremba, I. Sutskever, and O. Vinyals · 2014
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The loss surfaces of multilayer networks
A. Choromanska, M. Henaff, M. Mathieu, G. B. Arous, and Y. LeCun · 2015
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BinaryConnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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Learning both weights and connections for efficient neural networks
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. E. Hinton · 2015
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Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
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Quantized neural networks: training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
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XNOR-Net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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DoReFa-Net: training low bitwidth convolutional neural networks with low bitwidth gradients
Data noising as smoothing in neural network language models
Z. Xie, S. I. Wang, J. Li, D. Lévy, A. Nie, D. Jurafsky, and A. Y. Ng · 2017
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Balanced quantization: An effective and efficient approach to quantized neural networks
S. Zhou, Y. Wang, H. Wen, Q. He, and Y. Zou · 2017
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Trained ternary quantization
C. Zhu, S. Han, H. Mao, and W. J. Dally · 2017
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Apaptive quantization of neural networks
S. Khoram and J. Li · 2018
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Viterbi-based pruning for sparse matrix with fixed and high index compression ratio
D. Lee, D. Ahn, T. Kim, P. I. Chuang, and J.-J. Kim · 2018
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Training wide residual networks for deployment using a single bit for each weight
M. D. McDonnell · 2018
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S. Zhou, Z. Ni, X. Zhou, H. Wen, Y. Wu, and Y. Zou · 2016
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Towards the limit of network quantization
Y. Choi, M. El-Khamy, and J. Lee · 2017
Cited alongside, same era.
Network sketching: exploiting binary structure in deep CNNs
Y. Guo, A. Yao, H. Zhao, and Y. Chen · 2017
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DSD: Dense-Sparse-Dense training for deep neural networks
S. Han, J. Pool, S. Narang, H. Mao, E. Gong, S. Tang, E. Elsen, P. Vajda, M. Paluri, J. Tran, B. Catanzaro, and W. J. Dally · 2017
Cited alongside, same era.
Exploring sparsity in recurrent neural networks
S. Narang, G. Diamos, S. Sengupta, and E. Elsen · 2017
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Model compression via distillation and quantization
A. Polino, R. Pascanu, and D. Alistarh · 2018
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Training and inference with integers in deep neural networks
S. Wu, G. Li, F. Chen, and L. Shi · 2018
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Alternating multi-bit quantization for recurrent neural networks
C. Xu, J. Yao, Z. Lin, W. Ou, Y. Cao, Z. Wang, and H. Zha · 2018
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