-
2
-
-
77954662106
-
The curse of highly variable functions for local kernel machines
-
Y. Bengio, O. Delalleau, and N. Le Roux. The curse of highly variable functions for local kernel machines. In NIPS, 2005.
-
(2005)
NIPS
-
-
Bengio, Y.1
Delalleau, O.2
Le Roux, N.3
-
4
-
-
33745805403
-
A fast learning algorithm for deep belief nets
-
G. E. Hinton, S. Osindero, and Yee-Whye Teh. A fast learning algorithm for deep belief nets. Neur. Comput., 18:1527-1554, 2006.
-
(2006)
Neur. Comput
, vol.18
, pp. 1527-1554
-
-
Hinton, G.E.1
Osindero, S.2
Teh, Y.-W.3
-
5
-
-
0000329993
-
Information processing in dynamical systems: Foundations of harmony theory
-
P. Smolensky. Information processing in dynamical systems: Foundations of harmony theory. In Parallel Distributed Processing, pages 194-281. 1986.
-
(1986)
Parallel Distributed Processing
, pp. 194-281
-
-
Smolensky, P.1
-
9
-
-
77956509090
-
Rectified linear units improve restricted Boltzmann machines
-
V. Nair and G. E. Hinton. Rectified linear units improve restricted Boltzmann machines. In ICML, 2010.
-
(2010)
ICML
-
-
Nair, V.1
Hinton, G.E.2
-
11
-
-
0013344078
-
Training products of experts by minimizing contrastive divergence
-
G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neur. Comput., 14:1771-1800, 2002.
-
(2002)
Neur. Comput
, vol.14
, pp. 1771-1800
-
-
Hinton, G.E.1
-
12
-
-
67651049775
-
Justifying and generalizing contrastive divergence
-
Y. Bengio and O. Delalleau. Justifying and generalizing contrastive divergence. Neur. Comput., 21:1601-1621, 2009.
-
(2009)
Neur. Comput
, vol.21
, pp. 1601-1621
-
-
Bengio, Y.1
Delalleau, O.2
-
13
-
-
84887100846
-
Training RBMs depending on the signs of the CD approximation of the log-likelihood derivatives
-
A. Fischer and C. Igel. Training RBMs depending on the signs of the CD approximation of the log-likelihood derivatives. In ESANN, 2011.
-
(2011)
ESANN
-
-
Fischer, A.1
Igel, C.2
-
15
-
-
0024220237
-
Auto-association by multilayer perceptrons and singular value decomposition
-
H. Bourlard and Y. Kamp. Auto-association by multilayer perceptrons and singular value decomposition. Biological Cybernetics, 59:291-294, 1988.
-
(1988)
Biological Cybernetics
, vol.59
, pp. 291-294
-
-
Bourlard, H.1
Kamp, Y.2
-
16
-
-
0024732792
-
Connectionist learning procedures
-
G. E. Hinton. Connectionist learning procedures. Artificial Intelligence, 40:185-234, 1989.
-
(1989)
Artificial Intelligence
, vol.40
, pp. 185-234
-
-
Hinton, G.E.1
-
17
-
-
0034153465
-
Nonlinear autoassociation is not equivalent to PCA
-
N. Japkowicz, S. J. Hanson, and M. A. Gluck. Nonlinear autoassociation is not equivalent to PCA. Neur. Comput., 12:531-545, 2000.
-
(2000)
Neur. Comput
, vol.12
, pp. 531-545
-
-
Japkowicz, N.1
Hanson, S.J.2
Gluck, M.A.3
-
18
-
-
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
-
19
-
-
50249093806
-
An empirical evaluation of deep architectures on problems with many factors of variation
-
H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio. An empirical evaluation of deep architectures on problems with many factors of variation. In ICML, 2007.
-
(2007)
ICML
-
-
Larochelle, H.1
Erhan, D.2
Courville, A.3
Bergstra, J.4
Bengio, Y.5
-
20
-
-
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
-
21
-
-
85161966246
-
Sparse feature learning for deep belief networks
-
M. Ranzato, Y-L. Boureau, and Y. LeCun. Sparse feature learning for deep belief networks. In NIPS, 2008.
-
(2008)
NIPS
-
-
Ranzato, M.1
Boureau, Y.-L.2
Lecun, Y.3
-
23
-
-
78149327741
-
Kernel methods for deep learning
-
Y. Cho and L. Saul. Kernel methods for deep learning. In NIPS, 2009.
-
(2009)
NIPS
-
-
Cho, Y.1
Saul, L.2
-
24
-
-
0347243182
-
Nonlinear component analysis as a kernel eigenvalue problem
-
B. Schölkopf, A. J. Smola, and K.-R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neur. Comput., 10:1299-1319, 1998.
-
(1998)
Neur. Comput
, vol.10
, pp. 1299-1319
-
-
Schölkopf, B.1
Smola, A.J.2
Müller, K.-R.3
-
26
-
-
1942516826
-
Kernel PLS-SVC for linear and nonlinear classification
-
R. Rosipal, L. J. Trejo, and B. Matthews. Kernel PLS-SVC for linear and nonlinear classification. In ICML, 2003.
-
(2003)
ICML
-
-
Rosipal, R.1
Trejo, L.J.2
Matthews, B.3
-
27
-
-
0000494466
-
Handwritten digit recognition with a back-propagation network
-
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Handwritten digit recognition with a back-propagation network. In NIPS, 1990.
-
(1990)
NIPS
-
-
Lecun, Y.1
Boser, B.2
Denker, J.S.3
Henderson, D.4
Howard, R.E.5
Hubbard, W.6
Jackel, L.D.7
-
28
-
-
0032203257
-
Gradient-based learning applied to document recognition
-
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86:2278-2324, 1998.
-
(1998)
Proceedings of the IEEE
, vol.86
, pp. 2278-2324
-
-
Lecun, Y.1
Bottou, L.2
Bengio, Y.3
Haffner, P.4
-
29
-
-
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
-
31
-
-
71149119164
-
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
-
H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In ICML, 2009.
-
(2009)
ICML
-
-
Lee, H.1
Grosse, R.2
Ranganath, R.3
Ng, A.Y.4
-
33
-
-
85162460675
-
Learning convolutional feature hierarchies for visual recognition
-
K. Kavukcuoglu, P. Sermanet, Y.-L. Boureau, K. Gregor, M. Mathieu, and Y. LeCun. Learning convolutional feature hierarchies for visual recognition. In NIPS. 2010.
-
(2010)
NIPS
-
-
Kavukcuoglu, K.1
Sermanet, P.2
Boureau, Y.-L.3
Gregor, K.4
Mathieu, M.5
LeCun, Y.6
-
34
-
-
67649219352
-
Learning long-range vision for autonomous off-road driving
-
R. Hadsell, P. Sermanet, J. Ben, A. Erkan, M. Scoffier, K. Kavukcuoglu, U. Muller, and Y. LeCun. Learning long-range vision for autonomous off-road driving. J. Field Robot., 26:120-144, 2009.
-
(2009)
J. Field Robot
, vol.26
, pp. 120-144
-
-
Hadsell, R.1
Sermanet, P.2
Ben, J.3
Erkan, A.4
Scoffier, M.5
Kavukcuoglu, K.6
Muller, U.7
LeCun, Y.8
-
35
-
-
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
-
36
-
-
56449095373
-
A unified architecture for natural language processing: Deep neural networks with multitask learning
-
R. Collobert and J. Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In ICML, 2008.
-
(2008)
ICML
-
-
Collobert, R.1
Weston, J.2
-
37
-
-
85162037149
-
Using deep belief nets to learn covariance kernels for gaussian processes
-
R. Salakhutdinov and G. E. Hinton. Using deep belief nets to learn covariance kernels for gaussian processes. In NIPS, 2008.
-
(2008)
NIPS
-
-
Salakhutdinov, R.1
Hinton, G.E.2
-
38
-
-
84886992819
-
Modeling pigeon behaviour using a conditional restricted Boltzmann machine
-
M. D. Zeiler, G. W. Taylor, N. F. Troje, and G. E. Hinton. Modeling pigeon behaviour using a conditional restricted Boltzmann machine. In ESANN, 2009.
-
(2009)
ESANN
-
-
Zeiler, M.D.1
Taylor, G.W.2
Troje, N.F.3
Hinton, G.E.4
-
39
-
-
33746600649
-
Reducing the dimensionality of data with neural networks
-
G. E. Hinton and R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313:504-507, 2006.
-
(2006)
Science
, vol.313
, pp. 504-507
-
-
Hinton, G.E.1
Salakhutdinov, R.2
-
41
-
-
84887042736
-
Using very deep autoencoders for content-based image retrieval
-
A. Krizhevsky and G. E. Hinton. Using very deep autoencoders for content-based image retrieval. In ESANN, 2011.
-
(2011)
ESANN
-
-
Krizhevsky, A.1
Hinton, G.E.2
-
42
-
-
84879850729
-
Factored 3-way restricted Boltzmann machines for modeling natural images
-
M. Ranzato, A. Krizhevsky, and G. E. Hinton. Factored 3-way restricted Boltzmann machines for modeling natural images. In AISTATS, 2010.
-
(2010)
AISTATS
-
-
Ranzato, M.1
Krizhevsky, A.2
Hinton, G.E.3
-
43
-
-
79551480483
-
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
-
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res., 2010.
-
(2010)
J. Mach. Learn. Res
-
-
Vincent, P.1
Larochelle, H.2
Lajoie, I.3
Bengio, Y.4
Manzagol, P.-A.5
-
44
-
-
77949522811
-
Why does unsupervised pre-training help deep learning?
-
D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio. Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res., 11:625-660, 2010.
-
(2010)
J. Mach. Learn. Res
, vol.11
, pp. 625-660
-
-
Erhan, D.1
Bengio, Y.2
Courville, A.3
Manzagol, P.-A.4
Vincent, P.5
Bengio, S.6
-
45
-
-
84893557505
-
On random weights and unsupervised feature learning
-
A. Saxe, P. W. Koh, Z. Chen, M. Bhand, B. Suresh, and A. Ng. On random weights and unsupervised feature learning. In NIPS WS8, 2010.
-
(2010)
NIPS WS8
-
-
Saxe, A.1
Koh, P.W.2
Chen, Z.3
Bhand, M.4
Suresh, B.5
Ng, A.6
-
46
-
-
85083866926
-
Unsupervised layer-wise model selection in deep neural networks
-
L. Arnold, H. Paugam-Moisy, and M. Sebag. Unsupervised layer-wise model selection in deep neural networks. In ECAI, 2010.
-
(2010)
ECAI
-
-
Arnold, L.1
Paugam-Moisy, H.2
Sebag, M.3
|