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Sparse methods and the use of Winograd convolutions are two orthogonal approaches, each of which significantly accelerates convolution computations in modern CNNs.
Arithmetic complexity of computations , volume 33
Winograd, Shmuel · 1980
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Optimal brain damage
LeCun, Yann, Denker, John S., and Solla, Sara A · 1989
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li · 2009
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Fast training of convolutional networks through ffts
Mathieu, Michael, Henaff, Mikael, and LeCun, Yann · 2013
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Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Yangqing, Shelhamer, Evan, Donahue, Jeff, Karayev, Sergey, Long, Jonathan, Girshick, Ross, Guadarrama, Sergio, and Darrell, Trevor · 2014
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Learning both Weights and Connections for Efficient Neural Networks
Han, Song, Pool, Jeff, Tran, John, and Dally, William J · 2015
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Sparse Convolutional Neural Networks
Liu, Baoyuan, Wang, Min, Foroosh, Hassan, Tappen, Marshall, and Penksy, Marianna · 2015
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Spectral Representations for Convolutional Neural Networks
Rippel, Oren, Snoek, Jasper, , and Adams, Ryan P · 2015
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Going Deeper with Convolutions
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew · 2015
Cited alongside, same era.
Fast Convolutional Nets with fbfft: A GPU Performance Evaluation
Vasilache, Nicolas, Johnson, Jeff, Mathieu, Michael, Chintala, Soumith, Piantino, Serkan, and LeCun, Yann · 2015
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Dynamic Network Surgery for Efficient DNNs
Guo, Yiwen, Yao, Anbang, and Chen, Yurong · 2016
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Deep Residual Learning for Image Recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2016
Fast Algorithms for Convolutional Neural Networks
Lavin, Andrew and Gray, Scott · 2016
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Fast ConvNets Using Group-wise Brain Damage
Lebedev, Vadim and Lempitsky, Victor · 2016
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Pruning of Winograd and FFT Based Convolution Algorithm
Liu, Xingyu and Turakhia, Yatish · 2016
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Zero and data reuse-aware fast convolution for deep neural networks on GPU
Park, Hyunsun, Kim, Dongyoung, Ahn, Junwhan, and Yoo, Sungjoo · 2016
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Learning Structured Sparsity in Deep Neural Networks
Wen, Wei, Wu, Chunpeng, Wang, Yandan, Chen, Yiran, and Li, Hai · 2016
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Efficient Sparse-Winograd Convolutional Neural Networks
Liu, Xingyu, Han, Song, Mao, Huizi, and Dally, William J · 2017
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Libxsmm: accelerating small matrix multiplications by runtime code generation
Heinecke, Alexander, Henry, Greg, Hutchinson, Maxwell, and Pabst, Hans · 2016
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Gradient-Based Learning Applied to Document Recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick
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The MNIST database of handwritten digits
LeCun, Yann, Cortes, Corinna, and Burges, Christopher JC
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Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Park, Jongsoo, Li, Sheng, Wen, Wei, Tang, Ping Tak Peter, Li, Hai, Chen, Yiran, and Dubey, Pradeep · 2017
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