-
1
-
-
85162069624
-
Phone recognition with the mean-covariance restricted Boltzmann machine
-
G. E. Dahl, M. Ranzato, A. Mohamed, and G. E. Hinton. Phone recognition with the mean-covariance restricted Boltzmann machine. In NIPS. 2010.
-
(2010)
NIPS
-
-
Dahl, G.E.1
Ranzato, M.2
Mohamed, A.3
Hinton, G.E.4
-
2
-
-
84863380535
-
Unsupervised feature learning for audio classification using convolutional deep belief networks
-
H. Lee, Y. Largman, P. Pham, and A. Y. Ng. Unsupervised feature learning for audio classification using convolutional deep belief networks. In NIPS. 2009.
-
(2009)
NIPS
-
-
Lee, H.1
Largman, Y.2
Pham, P.3
Ng, A.Y.4
-
3
-
-
70450209196
-
Linear spatial pyramid matching using sparse coding for image classification
-
J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR, 2009.
-
(2009)
CVPR
-
-
Yang, J.1
Yu, K.2
Gong, Y.3
Huang, T.4
-
4
-
-
34948870900
-
Unsupervised learning of invariant feature hierarchies with applications to object recognition
-
M.A. Ranzato, F. J. Huang, Y.-L. Boureau, and Y. LeCun. Unsupervised learning of invariant feature hierarchies with applications to object recognition. In CVPR, 2007.
-
(2007)
CVPR
-
-
Ranzato, M.A.1
Huang, F.J.2
Boureau, Y.-L.3
Lecun, Y.4
-
5
-
-
80052874098
-
Learning hierarchical spatio-temporal features for action recognition with independent subspace analysis
-
Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng. Learning hierarchical spatio-temporal features for action recognition with independent subspace analysis. In CVPR, 2011.
-
(2011)
CVPR
-
-
Le, Q.V.1
Zou, W.Y.2
Yeung, S.Y.3
Ng, A.Y.4
-
6
-
-
85161980001
-
Sparse deep belief net model for visual area v2
-
H. Lee, C. Ekanadham, and A.Y. Ng. Sparse deep belief net model for visual area v2. In NIPS, 2008.
-
(2008)
NIPS
-
-
Lee, H.1
Ekanadham, C.2
Ng, A.Y.3
-
7
-
-
33745805403
-
A fast learning algorithm for deep belief nets
-
G. E. Hinton, S.Osindero, and Y.W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006.
-
(2006)
Neural Computation
, vol.18
, Issue.7
, pp. 1527-1554
-
-
Hinton, G.E.1
Osindero, S.2
Teh, Y.W.3
-
8
-
-
56449089103
-
Extracting and composing robust features with denoising autoencoders
-
P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol. Extracting and composing robust features with denoising autoencoders. In ICML, 2008.
-
(2008)
ICML
-
-
Vincent, P.1
Larochelle, H.2
Bengio, Y.3
Manzagol, P.A.4
-
9
-
-
0032492432
-
Independent component filters of natural images compared with simple cells in primary visual cortex
-
J. H. van Hateren and A. van der Schaaf. Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings: Biological Sciences, 265(1394):359-366, 1998.
-
(1998)
Proceedings: Biological Sciences
, vol.265
, Issue.1394
, pp. 359-366
-
-
Van Hateren, J.H.1
Van Der Schaaf, A.2
-
10
-
-
0030832881
-
The "independent components" of natural scenes are edge filters
-
December
-
A. J. Bell and T. J. Sejnowski. The "independent components" of natural scenes are edge filters. Vision Res., 37(23):3327-3338, December 1997.
-
(1997)
Vision Res.
, vol.37
, Issue.23
, pp. 3327-3338
-
-
Bell, A.J.1
Sejnowski, T.J.2
-
11
-
-
85162476392
-
Sparse coding with an overcomplete basis set: A strategy employed by V1?
-
B.Olshausen and D. Field. Sparse coding with an overcomplete basis set: A strategy employed by V1? Nature, 1997.
-
(1997)
Nature
-
-
Olshausen, B.1
Field, D.2
-
13
-
-
0000929221
-
What is the goal of sensory coding?
-
July
-
D. J. Field. What is the goal of sensory coding? Neural Computation, 6(4):559-601, July 1994.
-
(1994)
Neural Computation
, vol.6
, Issue.4
, pp. 559-601
-
-
Field, D.J.1
-
14
-
-
0005713456
-
Characterizing the sparseness of neural codes
-
January
-
B. Willmore and D. J. Tolhurst. Characterizing the sparseness of neural codes. Network, 12(3):255-270, January 2001.
-
(2001)
Network
, vol.12
, Issue.3
, pp. 255-270
-
-
Willmore, B.1
Tolhurst, D.J.2
-
15
-
-
0034939633
-
Natural signal statistics and sensory gain control
-
O. Schwartz and E. P. Simoncelli. Natural signal statistics and sensory gain control. Nature Neuroscience, 4:819-825, 2001.
-
(2001)
Nature Neuroscience
, vol.4
, pp. 819-825
-
-
Schwartz, O.1
Simoncelli, E.P.2
-
16
-
-
85112276587
-
Efficient learning of sparse representations with an energy-based model
-
M.A. Ranzato, C. Poultney, S. Chopra, and Y. Lecun. Efficient learning of sparse representations with an energy-based model. In NIPS, 2006.
-
(2006)
NIPS
-
-
Ranzato, M.A.1
Poultney, C.2
Chopra, S.3
Lecun, Y.4
-
17
-
-
80053446757
-
An analysis of single-layer networks in unsupervised feature learning
-
A. Coates, H. Lee, and A. Y. Ng. An analysis of single-layer networks in unsupervised feature learning. In AISTATS, 2011.
-
(2011)
AISTATS
-
-
Coates, A.1
Lee, H.2
Ng, A.Y.3
-
18
-
-
0000216612
-
What determines the capacity of autoassociative memories in the brain?
-
A. Treves and E. Rolls. What determines the capacity of autoassociative memories in the brain? Network: Computation in Neural Systems, 2:371-397(27), 1991.
-
(1991)
Network: Computation in Neural Systems
, vol.2
, Issue.27
, pp. 371-397
-
-
Treves, A.1
Rolls, E.2
-
20
-
-
85162517127
-
-
M. Schmidt. minFunc. http://www.cs.ubc.ca/~schmidtm/Software/minFunc. html, 2005.
-
(2005)
-
-
Schmidt, M.1
-
21
-
-
77955989954
-
Modeling pixel means and covariances using factorized third-order boltzmann machines
-
M. Ranzato and G. E. Hinton. Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines. In CVPR, 2010.
-
(2010)
CVPR
-
-
Ranzato, M.1
Hinton, G.E.2
-
22
-
-
78149334300
-
A two-layer model of natural stimuli estimated with score matching
-
U. Köster and A. Hyvärinen. A two-layer model of natural stimuli estimated with score matching. Neural Computation, 22(9):2308-2333, 2010.
-
(2010)
Neural Computation
, vol.22
, Issue.9
, pp. 2308-2333
-
-
Köster, U.1
Hyvärinen, A.2
-
23
-
-
80053448548
-
On random weights and unsupervised feature learning
-
A. Saxe, M. Bhand, Z. Chen, P.W. Koh, B. Suresh, and A.Y. Ng.On random weights and unsupervised feature learning. In ICML, 2011.
-
(2011)
ICML
-
-
Saxe, A.1
Bhand, M.2
Chen, Z.3
Koh, P.W.4
Suresh, B.5
Ng, A.Y.6
-
25
-
-
33947686745
-
Large margin gaussian mixture modeling for phonetic classification and recognition
-
IEEE
-
F. Sha and L.K. Saul. Large margin gaussian mixture modeling for phonetic classification and recognition. In ICASSP. IEEE, 2006.
-
(2006)
ICASSP
-
-
Sha, F.1
Saul, L.K.2
-
26
-
-
70450164063
-
Hidden conditional random field with distribution constraints for phone classification
-
D. Yu, L. Deng, and A. Acero. Hidden conditional random field with distribution constraints for phone classification. In Interspeech, 2009.
-
(2009)
Interspeech
-
-
Yu, D.1
Deng, L.2
Acero, A.3
-
29
-
-
0032639886
-
On the use of support vector machines for phonetic classification
-
P. Clarkson and P. J. Moreno.On the use of support vector machines for phonetic classification. Acoustics, Speech, and Signal Processing, IEEE International Conference on, 2:585-588, 1999.
-
(1999)
Acoustics, Speech, and Signal Processing, IEEE International Conference on
, vol.2
, pp. 585-588
-
-
Clarkson, P.1
Moreno, P.J.2
-
31
-
-
79955702502
-
LIBSVM: A library for support vector machines
-
Software
-
C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
-
(2011)
ACM Transactions on Intelligent Systems and Technology
, vol.2
, pp. 271-2727
-
-
Chang, C.-C.1
Lin, C.-J.2
-
33
-
-
77953183471
-
What is the best multi-stage architecture for object recognition?
-
K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun. What is the best multi-stage architecture for object recognition? In ICCV, 2009.
-
(2009)
ICCV
-
-
Jarrett, K.1
Kavukcuoglu, K.2
Ranzato, M.3
Lecun, Y.4
-
34
-
-
38949193299
-
Why is Real-World visual object recognition hard?
-
January
-
N. Pinto, D. D. Cox, and J. J. DiCarlo. Why is Real-World visual object recognition hard? PLoS Comput Biol, 4(1):e27+, January 2008.
-
(2008)
PLoS Comput Biol
, vol.4
, Issue.1
-
-
Pinto, N.1
Cox, D.D.2
Dicarlo, J.J.3
-
35
-
-
84900510076
-
Non-negative matrix factorization with sparseness constraints
-
PatrikO. Hoyer. Non-negative matrix factorization with sparseness constraints. JMLR, 5:1457-1469, 2004.
-
(2004)
JMLR
, vol.5
, pp. 1457-1469
-
-
Hoyer, PatrikO.1
|