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The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the network parameters.
Approximate nearest neighbors: Towards removing the curse of dimensionality
P. Indyk and R. Motwani · 1998
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Database-friendly random projections: Johnson-Lindenstrauss with binary coins
D. Achlioptas · 2003
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Finding frequent items in data streams
M. Charikar, K. Chen, and M. Farach-Colton · 2004
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Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
H. Jaeger and H. Haas · 2004
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Probability and Computing: Randomized Algorithms and Probabilistic Analysis
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The Fast Johnson Lindenstrauss Transform and approximate nearest neighbors
N. Ailon and B. Chazelle · 2009
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Kernel methods for deep learning
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Feature hashing for large scale multitask learning
K. Weinberger, A. Dasgupta, J. Langford, A. Smola, and J. Attenberg · 2009
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Hardware accelerated convolutional neural networks for synthetic vision systems
C. Farabet, B. Martini, P. Akselrod, S. Talay, Y. LeCun, and E. Culurciello · 2010
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On random weights and unsupervised feature learning
A. Saxe, P. W. Koh, Z. Chen, M. Bhand, B. Suresh, and A. Ng · 2011
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Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
G. Cormode, M. Garofalakis, P. J. Haas, and C. Jermaine · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Predicting parameters in deep learning
M. Denil, B. Shakibi, L. Dinh, M. Ranzato, and N. de Freitas · 2013
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Fastfood – approximating kernel expansions in loglinear time
Q. Le, T. Sarlós, and A. Smola · 2013
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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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Memory bounded deep convolutional networks
M. D. Collins and P. Kohli · 2014
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Scalable kernel methods via doubly stochastic gradients
B. Dai, B. Xie, N. He, Y. Liang, A. Raj, M. Balcan, and L. Song · 2014
Convolutional kernel networks
J. Mairal, P. Koniusz, Z. Harchaoui, and C. Schmid · 2014
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Learning by stretching deep networks
G. Pandey and A. Dukkipati · 2014
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ImageNet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2014
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Speeding up neural networks for large scale classification using WTA hashing
A. H. Bakhtiary, À. Lapedriza, and D. Masip · 2015
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Exploiting linear structure within convolutional networks for efficient evaluation
E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus · 2014
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Kernel methods match deep neural networks on timit
P.-S. Huang, H. Avron, T. N. Sainath, V. Sindhwani, and B. Ramabhadran · 2014
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Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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One weird trick for parallelizing convolutional neural networks
A. Krizhevsky · 2014
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Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification
H. Li, R. Zhao, and X. Wang · 2014
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Weight uncertainty in neural networks
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
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Compressing neural networks with the hashing trick
W. Chen, J. T. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen · 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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G. E. Hinton, O. Vinyals, and J. Dean · 2015
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Sparse convolutional neural networks
B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky · 2015
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FitNets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2015
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