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It has recently been observed that certain extremely simple feature encoding techniques are able to achieve state of the art performance on several standard image classification benchmarks including deep belief networks, convolutional nets, factored RBMs, mcRBMs, convolutional RBMs, sparse autoencoders and several others.
Two-point step size gradient methods
J. Barzilai and J. Borwein · 1988
Earlier work this paper cites.
Parallel and distributed computation: numerical methods
D. Bertsekas and J. Tsitsiklis · 1989
Earlier work this paper cites.
Handwritten digit recognition: Applications of neural net chips and automatic learning
Y. LeCun, L. Jackel, B. Boser, J. Denker, H. Graf, I. Guyon, D. Henderson, R. Howard, and W. Hubbard · 1989
Earlier work this paper cites.
Nonlinear Programming: Second Edition
D. Bertsekas · 1995
Earlier work this paper cites.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
B. Olshausen and D. Field · 1996
Earlier work this paper cites.
The “independent components” of natural scenes are edge filters
A. Bell and T. Sejnowski · 1997
Earlier work this paper cites.
Non-negative sparse coding
P. Hoyer · 2002
Earlier work this paper cites.
Regularization and variable selection via the elastic net
H. Zou and T. Hastie · 2005
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
G. Hinton, S. Osindero, and Y. Teh · 2006
Earlier work this paper cites.
Boosted lasso
P. Zhao and B. Yu · 2007
Earlier work this paper cites.
Kernel codebooks for scene categorization
J. van Gemert, J. Geusebroek, C. Veenman, and A. Smeulders · 2008
Earlier work this paper cites.
A fast iterative shrinkage-threshold algorithm for linear inverse problems
A. Beck and M. Teboulle · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Sparse reconstruction by separable approximation
S. Wright, R. Nowak, and M. Figueiredo · 2009
Earlier work this paper cites.
Learning mid-level features for recognition
Y-L. Boureau, F. Bach, LeCun Y., and J. Ponce · 2010
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Learning fast approximations of sparse coding
K. Gregor and Y. LeCun · 2010
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Learning to represent visual input
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Proximal methods for sparse hierarchical dictionary learning
R. Jenatton, J. Mairal, G. Obozinski, and Bach F · 2010
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Learning to detect roads in high-resolution aerial images
V. Mnih and G. Hinton · 2010
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Modelling pixel means and covariances using factored third-order boltzmann machines
M. Ranzato and G. Hinton · 2010
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Structured sparse coding via lateral inhibition
K. Gregor, A. Szlam, and Y. LeCun · 2011
Later among the works it cites.
Proximal methods for hierarchical sparse coding
R. Jenatton, J. Mairal, G. Obozinski, and Bach F · 2011
Later among the works it cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Ng · 2011
Later among the works it cites.
Sparse filtering
J. Ngiam, P. Koh, Z. Chen, S. Bhaskar, and A. Ng · 2011
Later among the works it cites.
On deep generative models with applications to recognition
M. Ranzato, J. Susskind, V. Mnih, and G. Hinton · 2011
Later among the works it cites.
The manifold tangent classifier
S. Rifai, Y. Dauphin, P. Vincent, Y. Bengio, and X. Muller · 2011
Later among the works it cites.
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On the applicability of unsupervised feature learning for object recognition in RGB-D data
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