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Current deep learning architectures are growing larger in order to learn from complex datasets.
Some methods of speeding up the convergence of iteration methods
B. T. Polyak · 1964
Earlier work this paper cites.
Learning representations by back-propagating errors
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1988
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
G. Cybenko · 1989
Earlier work this paper cites.
On the approximate realization of continuous mappings by neural networks
K.-I. Funahashi · 1989
Earlier work this paper cites.
Approximate nearest neighbors: Towards removing the curse of dimensionality
P. Indyk and R. Motwani · 1998
Earlier work this paper cites.
Similarity search in high dimensions via hashing
A. Gionis, P. Indyk, R. Motwani, et al · 1999
Earlier work this paper cites.
Database-friendly random projections
D. Achlioptas · 2001
Earlier work this paper cites.
E2lsh: Exact euclidean locality sensitive hashing
A. Andoni and P. Indyk · 2004
Earlier work this paper cites.
Learning methods for generic object recognition with invariance to pose and lighting
Y. LeCun, F. J. Huang, and L. Bottou · 2004
Earlier work this paper cites.
Very sparse random projections
P. Li, T. J. Hastie, and K. W. Church · 2006
Earlier work this paper cites.
An empirical evaluation of deep architectures on problems with many factors of variation
H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio · 2007
Earlier work this paper cites.
Training invariant support vector machines using selective sampling
G. Loosli, S. Canu, and L. Bottou · 2007
Earlier work this paper cites.
Multi-probe lsh: efficient indexing for high-dimensional similarity search
Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li · 2007
Earlier work this paper cites.
Introduction to algorithms
T. H. Cormen · 2009
Earlier work this paper cites.
Similarity search and locality sensitive hashing using ternary content addressable memories
R. Shinde, A. Goel, P. Gupta, and D. Dutta · 2010
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
J. Duchi, E. Hazan, and Y. Singer · 2011
Cited alongside, same era.
Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
Cited alongside, same era.
Hogwild: A lock-free approach to parallelizing stochastic gradient descent
B. Recht, C. Re, S. Wright, and F. Niu · 2011
Cited alongside, same era.
Large scale distributed deep networks
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, A. Senior, P. Tucker, K. Yang, Q. V. Le, et al · 2012
Cited alongside, same era.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Later among the works it cites.
Always-on vision processing unit for mobile applications
B. Barry, C. Brick, F. Connor, D. Donohoe, D. Moloney, R. Richmond, M. O’Riordan, and V. Toma · 2015
Later among the works it cites.
Compressing neural networks with the hashing trick
W. Chen, J. T. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen · 2015
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Later among the works it cites.
Query-aware locality-sensitive hashing for approximate nearest neighbor search
Q. Huang, J. Feng, Y. Zhang, Q. Fang, and W. Ng · 2015
Later among the works it cites.
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Fast near neighbor search in high-dimensional binary data
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Cited alongside, same era.
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J. Ba and B. Frey · 2013
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A. Makhzani and B. Frey · 2013
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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A. Makhzani and B. J. Frey · 2015
Later among the works it cites.
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A. Shrivastava · 2015
Later among the works it cites.
Asymmetric minwise hashing for indexing binary inner products and set containment
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Later among the works it cites.
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