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We present flattened convolutional neural networks that are designed for fast feedforward execution.
Effiicient backprop
LeCun, Yann, Bottou, Léon, Orr, Genevieve B., and Müller, Klaus-Robert · 1996
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Efficient sparse coding algorithms
Lee, Honglak, Battle, Alexis, Raina, Rajat, and Ng, Andrew Y · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
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Torch7: A matlab-like environment for machine learning
Collobert, Ronan, Kavukcuoglu, Koray, and Farabet, Clément · 2011
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Kernel analysis of deep networks
Montavon, Grégoire, Braun, Mikio, and Müller, Klaus-Robert · 2011
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Improving the speed of neural networks on CPUs
Vanhoucke, Vincent, Senior, Andrew, and Mao, Mark Z · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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An analysis of the connections between layers of deep neural networks
Culurciello, Eugenio, Jin, Jonghoon, Dundar, Aysegul, and Bates, Jordan · 2013
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Predicting parameters in deep learning
Denil, Misha, Shakibi, Babak, Dinh, Laurent, de Freitas, Nando, et al · 2013
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Goodfellow, Ian J, Warde-Farley, David, Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua · 2013
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Lin, Min, Chen, Qiang, and Yan, Shuicheng · 2013
Cited alongside, same era.
Learning separable filters
Rigamonti, R., Sironi, A., Lepetit, V., and Fua, P · 2013
Cited alongside, same era.
Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, Pierre, Eigen, David, Zhang, Xiang, Mathieu, Michaël, Fergus, Rob, and LeCun, Yann · 2013
Cited alongside, same era.
Compressing deep convolutional networks using vector quantization
Gong, Yunchao, Liu, Liu, Yang, Ming, and Bourdev, Lubomir D · 2014
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Speeding up convolutional neural networks with low rank expansions
Jaderberg, Max, Vedaldi, Andrea, and Zisserman, Andrew · 2014
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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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An efficient implementation of deep convolutional neural networks on a mobile coprocessor
Jin, Jonghoon, Gokhale, Vinayak, Dundar, Aysegul, Krishnamurthy, Bharadwaj, Martini, Berin, and Culurciello, Eugenio · 2014
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
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Discriminative transfer learning with tree-based priors
Srivastava, Nitish and Salakhutdinov, Ruslan · 2013
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cudnn: Efficient primitives for deep learning
Chetlur, Sharan, Woolley, Cliff, Vandermersch, Philippe, Cohen, Jonathan, Tran, John, Catanzaro, Bryan, and Shelhamer, Evan · 2014
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Exploiting linear structure within convolutional networks for efficient evaluation
Denton, Emily L, Zaremba, Wojciech, Bruna, Joan, LeCun, Yann, and Fergus, Rob · 2014
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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick
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Lebedev, Vadim, Ganin, Yaroslav, Rakhuba, Maksim, Oseledets, Ivan V., and Lempitsky, Victor S · 2014
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A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, P.A., Arthur, J.V., Alvarez-Icaza, R., Cassidy, A.S., Sawada, J., Akopyan, F., Jackson, B.L., Imam, N., Guo, C., Nakamura, Y., Brezzo, B., Vo, I., Esser, S.K., Appuswamy, R., Taba, B., Amir, A., Flickner, M.D., Risk, W.P., Manohar, R., and Modha, D.S · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, Karen and Zisserman, Andrew · 2014
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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 · 2014
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