-
1
-
-
0042565834
-
Hierarchical Bayesian inference in the visual cortex
-
Jul., and
-
T. S. Lee, and D. Mumford, “Hierarchical Bayesian inference in the visual cortex,” The Journal of the Optical Society of America A, Vol. 20, no. 7, pp. 1434–48, Jul. 2003.
-
(2003)
The Journal of the Optical Society of America A
, vol.20
, Issue.7
, pp. 1434-1448
-
-
Lee, T.S.1
Mumford, D.2
-
2
-
-
33847380121
-
Robust object recognition with cortex-like mechanisms
-
Mar., and
-
T. Serre, L. Wolf, and S. Bileschi, “Robust object recognition with cortex-like mechanisms,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 29, no. 3, pp. 411–26, Mar. 2007.
-
(2007)
IEEE Trans. on Pattern Analysis and Machine Intelligence
, vol.29
, Issue.3
, pp. 411-426
-
-
Serre, T.1
Wolf, L.2
Bileschi, S.3
-
3
-
-
0031875590
-
The role of the primary visual cortex in higher level vision
-
Aug., and
-
T. S. Lee, D. Mumford, and R. Romero, “The role of the primary visual cortex in higher level vision,” Vision Research, Vol. 38, no. 15, pp. 2429–54, Aug. 1998.
-
(1998)
Vision Research
, vol.38
, Issue.15
, pp. 2429-2454
-
-
Lee, T.S.1
Mumford, D.2
Romero, R.3
-
4
-
-
0022471098
-
Learning representations by back-propagating errors
-
Oct., and
-
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, Vol. 323, no. 7, pp. 533–6, Oct. 1986.
-
(1986)
Nature
, vol.323
, Issue.7
, pp. 533-536
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
5
-
-
84864073449
-
Greedy layer-wise training of deep networks
-
Schölkopf B., Platt J.C., Hoffman T., (eds), Cambridge, MA: MIT Press, and, Eds
-
Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layer-wise training of deep networks,” in Advances in Neural Information Processing Systems, Vol. 19, B. Schölkopf, J. C. Platt and T. Hoffman, Eds. Cambridge, MA: MIT Press, 2006, pp. 153–60.
-
(2006)
Advances in Neural Information Processing Systems
, vol.19
, pp. 153-160
-
-
Bengio, Y.1
Lamblin, P.2
Popovici, D.3
Larochelle, H.4
-
6
-
-
59449087310
-
Exploring strategies for training deep neural networks
-
Jan., and
-
H. Larochelle, Y. Bengio, J. Louradour, and P. Lamblin, “Exploring strategies for training deep neural networks,” Journal of Machine Learning Research, Vol. 1, pp. 1–40, Jan. 2009.
-
(2009)
Journal of Machine Learning Research
, vol.1
, pp. 1-40
-
-
Larochelle, H.1
Bengio, Y.2
Louradour, J.3
Lamblin, P.4
-
8
-
-
0000783715
-
Replicator neural networks for universal optimal source coding
-
Sept
-
R. Hecht-Nielsen, “Replicator neural networks for universal optimal source coding,” Science, Vol. 269, pp. 1860–3, Sept. 1995.
-
(1995)
Science
, vol.269
, pp. 1860-1863
-
-
Hecht-Nielsen, R.1
-
9
-
-
0001046225
-
Practical issues in temporal difference learning
-
May
-
G. Tesauro, “Practical issues in temporal difference learning,” Machine Learning, Vol. 8, no. 3–4, pp. 257–77, May 1992.
-
(1992)
Machine Learning
, vol.8
, Issue.3-4
, pp. 257-277
-
-
Tesauro, G.1
-
10
-
-
69349090197
-
Learning deep architectures for AI
-
Jan
-
Y. Bengio, “Learning deep architectures for AI,” Foundations and trends® in Machine Learning, Vol. 2, no. 1, pp. 1–127, Jan. 2009.
-
(2009)
Foundations and trends® in Machine Learning
, vol.2
, Issue.1
, pp. 1-127
-
-
Bengio, Y.1
-
11
-
-
34547975052
-
Scaling learning algorithms towards AI
-
Sept., and
-
Y. Bengio, and Y. LeCun, “Scaling learning algorithms towards AI,” Large-Scale Kernel Machines, Vol. 34, pp. 1–41, Sept. 2007.
-
(2007)
Large-Scale Kernel Machines
, vol.34
, pp. 1-41
-
-
Bengio, Y.1
LeCun, Y.2
-
12
-
-
33745805403
-
A learning algorithm for deep belief nets
-
Jul., and Y. W. The
-
G. E. Hinton, S. Osindero, and Y. W. The, “A learning algorithm for deep belief nets,” Neural Computation, Vol. 18, no. 7, pp. 1527–54, Jul. 2006.
-
(2006)
Neural Computation
, vol.18
, Issue.7
, pp. 1527-1554
-
-
Hinton, G.E.1
Osindero, S.2
-
13
-
-
1942418618
-
Discriminative probabilistic models for relational data
-
Alberta: 2002, and
-
B. Taskar, P. Abbeel, and D. Koller, “Discriminative probabilistic models for relational data,” in Proceedings of Conference on Uncertainty in Artificial Intelligence, Alberta, 2002, pp. 485–92.
-
Proceedings of Conference on Uncertainty in Artificial Intelligence
, pp. 485-492
-
-
Taskar, B.1
Abbeel, P.2
Koller, D.3
-
14
-
-
33845597672
-
Principled hybrids of generative and discriminative models
-
New York: 2006, and
-
J. A. Lasserre, C. M. Bishop, and T. P. Minka, “Principled hybrids of generative and discriminative models,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, 2006, pp. 87–94.
-
Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
, pp. 87-94
-
-
Lasserre, J.A.1
Bishop, C.M.2
Minka, T.P.3
-
16
-
-
0029372831
-
The Helmholtz machine
-
Sept., and
-
P. Dayan, G. E. Hinton, R. Neal, and R. Zemel. “The Helmholtz machine,”Neural Computation, Vol. 7, no. 5, pp. 889–904, Sept. 1995.
-
(1995)
Neural Computation
, vol.7
, Issue.5
, pp. 889-904
-
-
Dayan, P.1
Hinton, G.E.2
Neal, R.3
Zemel, R.4
-
17
-
-
0029652445
-
The “wake-sleep” algorithm for unsupervised neural network
-
May, and
-
G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal, “The “wake-sleep” algorithm for unsupervised neural network,” Science, Vol. 268, no. 5214, pp. 1558–161, May 1995.
-
(1995)
Science
, vol.268
, Issue.5214
, pp. 1558-1161
-
-
Hinton, G.E.1
Dayan, P.2
Frey, B.J.3
Neal, R.M.4
-
18
-
-
0029679189
-
Mean field theory for sigmoid belief networks
-
Jan., and
-
L. K. Saul, T. Jaakkola, and M. I. Jordan, “Mean field theory for sigmoid belief networks,” Journal of Artificial Intelligence Research, Vol. 4, no. 1, pp. 61–76, Jan. 1996.
-
(1996)
Journal of Artificial Intelligence Research
, vol.4
, Issue.1
, pp. 61-76
-
-
Saul, L.K.1
Jaakkola, T.2
Jordan, M.I.3
-
19
-
-
84860516367
-
Constituent parsing with incremental sigmoid belief networks
-
Prague: 2007, and
-
I. Titov, and J. Henderson, “Constituent parsing with incremental sigmoid belief networks,” in Proceedings of Meeting of Association for Computational Linguistics, Prague, 2007, pp. 632–9.
-
Proceedings of Meeting of Association for Computational Linguistics
, pp. 632-639
-
-
Titov, I.1
Henderson, J.2
-
21
-
-
0345368881
-
Unsupervised learning of distributions on binary vectors using two layer networks
-
Moody J.E., Hanson S.J., Lippmann R.P., (eds), Denver, CO: Morgan Kaufmann, and, Eds
-
Y. Freund, and D. Haussler, “Unsupervised learning of distributions on binary vectors using two layer networks,” in Advances in Neural Information Processing Systems, Vol. 4, J. E. Moody, S. J. Hanson, and R.P. Lippmann, Eds. Denver, CO: Morgan Kaufmann, 1991, pp. 912–9.
-
(1991)
Advances in Neural Information Processing Systems
, vol.4
, pp. 912-919
-
-
Freund, Y.1
Haussler, D.2
-
22
-
-
0013344078
-
Training products of experts by minimizing contrastive divergence
-
Aug
-
G. E. Hinton, “Training products of experts by minimizing contrastive divergence,”Neural Computation, Vol. 14, no. 8, pp. 1771–800, Aug. 2002.
-
(2002)
Neural Computation
, vol.14
, Issue.8
, pp. 1771-1800
-
-
Hinton, G.E.1
-
23
-
-
33745780732
-
Exponential family harmoniums with an application to information retrieval
-
Saul L.K., Weiss Y., Bottou L., (eds), Cambridge, MA: MIT Press, and, Eds
-
M. Welling, M. Rosen-Zvi, and G. E. Hinton, “Exponential family harmoniums with an application to information retrieval,” in Advances in Neural Information Processing Systems, Vol. 17, L. K. Saul, Y. Weiss and L. Bottou, Eds. Cambridge, MA: MIT Press, 2004, pp. 1481–8.
-
(2004)
Advances in Neural Information Processing Systems
, vol.17
, pp. 1481-1488
-
-
Welling, M.1
Rosen-Zvi, M.2
Hinton, G.E.3
-
24
-
-
84862286946
-
Deep Boltzmann machines
-
Florida: 2009, and
-
R. Salakhutdinov, and G. E. Hinton, “Deep Boltzmann machines,” in Proceedings of International Conference on Artificial Intelligence and Statistics, Florida, 2009, pp. 448–55.
-
Proceedings of International Conference on Artificial Intelligence and Statistics
, pp. 448-455
-
-
Salakhutdinov, R.1
Hinton, G.E.2
-
25
-
-
33746600649
-
Reducing the dimensionality of data with neural networks
-
May, and
-
G. E. Hinton, and R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, Vol. 313, no. 5786, pp. 504–7, May 2006.
-
(2006)
Science
, vol.313
, Issue.5786
, pp. 504-507
-
-
Hinton, G.E.1
Salakhutdinov, R.2
-
26
-
-
56449095373
-
A unified architecture for natural language processing: Deep neural networks with multitask learning
-
Helsinki: 2008, and
-
R. Collobert, and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proceedings of International Conference on Machine learning, Helsinki, 2008, pp. 160–7.
-
Proceedings of International Conference on Machine learning
, pp. 160-167
-
-
Collobert, R.1
Weston, J.2
-
27
-
-
84864069017
-
Efficient learning of sparse representations with an energy-based model
-
Schölkopf B., Platt J.C., Hoffman T., (eds), Cambridge: MIT Press, and, Eds
-
M. Ranzato, C. Poultney, S. Chopra, and Y. LeCun, “Efficient learning of sparse representations with an energy-based model,” in Advances in Neural Information Processing Systems, Vol. 19, B. Schölkopf, J. C. Platt and T. Hoffman, Eds. Cambridge: MIT Press, 2006, pp. 1137–44.
-
(2006)
Advances in Neural Information Processing Systems
, vol.19
, pp. 1137-1144
-
-
Ranzato, M.1
Poultney, C.2
Chopra, S.3
LeCun, Y.4
-
28
-
-
84945900998
-
Best practices for convolutional neural networks applied to visual document analysis
-
Washington DC: 2003, and
-
P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” in Proceedings of the 7th International Conference on Document Analysis and Recognition, Washington DC, 2003, pp. 958–63.
-
Proceedings of the 7th International Conference on Document Analysis and Recognition
, pp. 958-963
-
-
Simard, P.Y.1
Steinkraus, D.2
Platt, J.C.3
-
30
-
-
79959388970
-
Parallel tempering is efficient for learning restricted Boltzmann machines
-
Thessaloniki:, and
-
K. H. Cho, T. Raiko, and A. Ilin, “Parallel tempering is efficient for learning restricted Boltzmann machines,” in Proceedings of the 2010 International Joint Conference on Neural Networks, Thessaloniki, 2010, pp. 1–8.
-
(2010)
Proceedings of the 2010 International Joint Conference on Neural Networks
, pp. 1-8
-
-
Cho, K.H.1
Raiko, T.2
Ilin, A.3
-
31
-
-
45749110924
-
Representational power of restricted Boltzmann machines and deep belief networks
-
Jun., and
-
N. Le Roux, and Y. Bengio, “Representational power of restricted Boltzmann machines and deep belief networks,” Neural Computation, Vol. 20, no. 6, pp. 1631–49, Jun. 2008.
-
(2008)
Neural Computation
, vol.20
, Issue.6
, pp. 1631-1649
-
-
Le Roux, N.1
Bengio, Y.2
-
32
-
-
78049383425
-
Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machines
-
Thessaloniki: 2010, and
-
A. Fischer, and C. Igel, “Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machines,” in Proceedings of the 20th International Conference on Artificial Neural Networks, Thessaloniki, 2010, pp. 208–17.
-
Proceedings of the 20th International Conference on Artificial Neural Networks
, pp. 208-217
-
-
Fischer, A.1
Igel, C.2
-
34
-
-
35348818718
-
Learning multiple layers of representation
-
Oct
-
G. E. Hinton, “Learning multiple layers of representation,” Trends in Cognitive Sciences, Vol. 11, no. 10, pp. 428–34, Oct. 2007.
-
(2007)
Trends in Cognitive Sciences
, vol.11
, Issue.10
, pp. 428-434
-
-
Hinton, G.E.1
-
35
-
-
56449086223
-
Training restricted Boltzmann machines using approximations to the likelihood gradient
-
New York: 2008
-
T. Tieleman, “Training restricted Boltzmann machines using approximations to the likelihood gradient,” in Proceedings of the 25th International Conference on Machine Learning, New York, 2008, pp. 1064–71.
-
Proceedings of the 25th International Conference on Machine Learning
, pp. 1064-1071
-
-
Tieleman, T.1
-
36
-
-
71149084943
-
Using fast weights to improve persistent contrastive divergence
-
New York: 2009, and
-
T. Tieleman, and G. Hinton, “Using fast weights to improve persistent contrastive divergence,” in Proceedings of the 26th Annual International Conference on Machine Learning, New York, 2009, pp. 1033–40.
-
Proceedings of the 26th Annual International Conference on Machine Learning
, pp. 1033-1040
-
-
Tieleman, T.1
Hinton, G.2
-
37
-
-
67651049775
-
Justifying and generalizing contrastive divergence
-
Jun., and
-
Y. Bengio, and O. Delalleau, “Justifying and generalizing contrastive divergence,” Neural Computation, Vol. 21, no. 6, pp. 1601–21, Jun. 2009.
-
(2009)
Neural Computation
, vol.21
, Issue.6
, pp. 1601-1621
-
-
Bengio, Y.1
Delalleau, O.2
-
38
-
-
78049383425
-
Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machines
-
Thessaloniki: 2010, and
-
A. Fischer, and C. Igel, “Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machines,” in Proceedings of the 20th International Conference on Artificial Neural Networks, Thessaloniki, 2010, pp. 208–17.
-
Proceedings of the 20th International Conference on Artificial Neural Networks
, pp. 208-217
-
-
Fischer, A.1
Igel, C.2
-
39
-
-
28844454252
-
Parallel tempering: theory, applications, and new perspectives
-
–, Aug
-
D. J. Earl, and M. W. Deem, “Parallel tempering: theory, applications, and new perspectives,” Physical Chemistry Chemical Physics, Vol. 7, pp. 3910–6, Aug. 2005.
-
(2005)
Physical Chemistry Chemical Physics
, vol.7
, pp. 3910-3916
-
-
Earl, D.J.1
Deem, M.W.2
-
40
-
-
84862293204
-
Parallel tempering for training of restricted Boltzmann machines
-
New York: 2010, and
-
G. Desjardins, A. Courville, and Y. Bengio, “Parallel tempering for training of restricted Boltzmann machines,” in Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, New York, 2010, pp. 145–52.
-
Proceedings of the 13th International Conference on Artificial Intelligence and Statistics
, pp. 145-152
-
-
Desjardins, G.1
Courville, A.2
Bengio, Y.3
-
41
-
-
21444452127
-
Sampling from multimodal distributions using tempered transitions
-
Dec
-
R. M. Neal, “Sampling from multimodal distributions using tempered transitions,” Statistics and Computing, Vol. 6, no. 4, pp. 353–66, Dec. 1996.
-
(1996)
Statistics and Computing
, vol.6
, Issue.4
, pp. 353-366
-
-
Neal, R.M.1
-
42
-
-
0035600220
-
Extended ensemble monte carlo
-
Jun
-
Y. Iba, “Extended ensemble monte carlo,” International Journal of Modern Physics, Vol. 12, no. 5, pp. 623–56, Jun. 2001.
-
(2001)
International Journal of Modern Physics
, vol.12
, Issue.5
, pp. 623-656
-
-
Iba, Y.1
-
43
-
-
84901064273
-
Improving Mixing Rate with Tempered Transition for Learning Restricted Boltzmann Machines
-
Sept., and
-
J. Xu, H. Li, and S. Zhou, “Improving Mixing Rate with Tempered Transition for Learning Restricted Boltzmann Machines,” Neurocomputing, Vol. 139, pp. 328–35, Sept. 2014.
-
(2014)
Neurocomputing
, vol.139
, pp. 328-335
-
-
Xu, J.1
Li, H.2
Zhou, S.3
-
44
-
-
0001969496
-
Learning sets of filters using back-propagation
-
Mar., and
-
D. C. Plaut, and G. E. Hinton, “Learning sets of filters using back-propagation,” Computer, Speech and Language, Vol. 2, no. 1, pp. 35–61, Mar. 1987.
-
(1987)
Computer, Speech and Language
, vol.2
, Issue.1
, pp. 35-61
-
-
Plaut, D.C.1
Hinton, G.E.2
-
45
-
-
0000362092
-
Non-linear dimension reduction
-
Hanson S.J., Cowan J.D., Giles C.L., (eds), San Mateo, CA: Morgan Kaufmann, and, Eds
-
D. DeMers, and G. Cottrell, “Non-linear dimension reduction,” in Advances in Neural Information Processing Systems, Vol. 5, S. J. Hanson, J. D. Cowan and C. L. Giles, Eds. San Mateo, CA: Morgan Kaufmann, 1992, pp. 580–7.
-
(1992)
Advances in Neural Information Processing Systems
, vol.5
, pp. 580-587
-
-
DeMers, D.1
Cottrell, G.2
-
46
-
-
0000783715
-
Replicator neural networks for universal optimal source coding
-
Sept
-
R. Hecht-Nielsen, “Replicator neural networks for universal optimal source coding,” Science, Vol. 269, no. 5232, pp. 1860–3, Sept. 1995.
-
(1995)
Science
, vol.269
, Issue.5232
, pp. 1860-1863
-
-
Hecht-Nielsen, R.1
-
47
-
-
0348139702
-
Dimension reduction by local principal component analysis
-
Oct., and
-
N. Kambhatla, and T. K. Leen, “Dimension reduction by local principal component analysis,” Neural Computation, Vol. 9, no. 7, pp. 1493–516, Oct. 1997.
-
(1997)
Neural Computation
, vol.9
, Issue.7
, pp. 1493-1516
-
-
Kambhatla, N.1
Leen, T.K.2
-
48
-
-
84862286946
-
Deep Boltzmann machines
-
Clearwater Beach: 2009, and
-
R. Salakhutdinov, and G. E. Hinton, “Deep Boltzmann machines,” in Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, Clearwater Beach, 2009, pp. 448–55.
-
Proceedings of the 12th International Conference on Artificial Intelligence and Statistics
, pp. 448-455
-
-
Salakhutdinov, R.1
Hinton, G.E.2
-
49
-
-
84887445321
-
A better way to pretrain deep Boltzmann machines
-
Pereira F., Burges C.J.C., Bottou L., Weinberger K.Q., (eds), Cambridge, MA: MIT Press, and, Eds
-
R. Salakhutdinov, and G. Hinton, “A better way to pretrain deep Boltzmann machines,” Advances in Neural Information Processing Systems, Vol. 25, F. Pereira, C. J. C. Burges, L. Bottou and K. Q. Weinberger, Eds. Cambridge, MA: MIT Press, 2012, pp. 1–9.
-
(2012)
Advances in Neural Information Processing Systems
, vol.25
, pp. 1-9
-
-
Salakhutdinov, R.1
Hinton, G.2
-
50
-
-
78651276374
-
Learning deep generative models
-
Graduate Department of Computer Science, Univ. Toronto, Toronto
-
R. Salakhutdinov, “Learning deep generative models,” Ph.D. Dissertation, Graduate Department of Computer Science, Univ. Toronto, Toronto, 2009.
-
(2009)
Ph.D. Dissertation
-
-
Salakhutdinov, R.1
-
51
-
-
84876458566
-
Facial expression recognition using local transitional pattern on gabor filtered facial images
-
–, Jan
-
A. Tanveer, J. Taskeed, and C. Ui-Pil, “Facial expression recognition using local transitional pattern on gabor filtered facial images”, IETE Technical Review, Vol. 30, no. 1, pp. 47–52, Jan. 2013.
-
(2013)
IETE Technical Review
, vol.30
, Issue.1
, pp. 47-52
-
-
Tanveer, A.1
Taskeed, J.2
Ui-Pil, C.3
-
52
-
-
33745805403
-
A fast learning algorithm for deep belief nets
-
Jul., and
-
G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, Vol. 18, no. 7, pp. 1527–54, Jul. 2006.
-
(2006)
Neural Computation
, vol.18
, Issue.7
, pp. 1527-1554
-
-
Hinton, G.E.1
Osindero, S.2
Teh, Y.W.3
-
53
-
-
85081846480
-
An EM-based multi-step piecewise surface regression learning algorithm
-
Las Vegas: 2011, and
-
J. Luo, and A. Brodsky, “An EM-based multi-step piecewise surface regression learning algorithm,” in Proceedings of the 7th International Conference on Data Mining, Las Vegas, 2011, pp. 286–92.
-
Proceedings of the 7th International Conference on Data Mining
, pp. 286-292
-
-
Luo, J.1
Brodsky, A.2
-
54
-
-
84866771105
-
An EM-based ensemble learning algorithm on piecewise surface regression problem
-
–, Aug
-
J. Luo, A. Brodsky, and Y. Li, “An EM-based ensemble learning algorithm on piecewise surface regression problem,” International Journal of Applied Mathematics and Statistics, Vol. 28, no. 4, pp. 59–74, Aug. 2012.
-
(2012)
International Journal of Applied Mathematics and Statistics
, vol.28
, Issue.4
, pp. 59-74
-
-
Luo, J.1
Brodsky, A.2
Li, Y.3
-
55
-
-
78149306047
-
3-d object recognition with deep belief nets
-
Bengio Y., Schuurmans D., Lafferty J.D., Williams C.K.I., Culotta A., (eds), Cambridge, MA: MIT Press, and, Eds
-
V. Nair, and G.Hinton, “3-d object recognition with deep belief nets,” in Advances in Neural Information Processing Systems, Vol. 22, Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams and A. Culotta, Eds. Cambridge, MA: MIT Press, 2009, pp. 1339–47.
-
(2009)
Advances in Neural Information Processing Systems
, vol.22
, pp. 1339-1347
-
-
Nair, V.1
Hinton, G.2
-
56
-
-
77956542104
-
Deep networks for robust visual recognition
-
Haifa: 2010, and
-
Y. Tang, and C. Eliasmith, “Deep networks for robust visual recognition,” in Proceedings of the 27th International Conference on Machine Learning, Haifa, 2010, pp. 1055–62.
-
Proceedings of the 27th International Conference on Machine Learning
, pp. 1055-1062
-
-
Tang, Y.1
Eliasmith, C.2
-
57
-
-
51949119257
-
Small codes and large image databases for recognition
-
Anchorage: 2008, and
-
A. Taralba, R. Fergus, and Y. Weiss, “Small codes and large image databases for recognition,” in Proceedings of Computer Vision and Pattern Recognition, Anchorage, 2008, pp. 1–8.
-
Proceedings of Computer Vision and Pattern Recognition
, pp. 1-8
-
-
Taralba, A.1
Fergus, R.2
Weiss, Y.3
-
58
-
-
80053437179
-
Multimodal deep learning
-
Bellevue: 2011, and
-
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Ng, “Multimodal deep learning,” in Proceedings of the 28th International Conference on Machine Learning, Bellevue, 2011, pp. 689–96.
-
Proceedings of the 28th International Conference on Machine Learning
, pp. 689-696
-
-
Ngiam, J.1
Khosla, A.2
Kim, M.3
Nam, J.4
Lee, H.5
Ng, A.6
-
59
-
-
84877724347
-
Multimodal learning with deep Boltzmann machines
-
Pereira F., Burges C.J.C., Bottou L., Weinberger K.Q., (eds), Montreal, Canada: NIPS, and, Eds
-
N. Srivastava, and R. Salakhutdinov, “Multimodal learning with deep Boltzmann machines,”in Advances in Neural Information Processing Systems, Vol. 25, F. Pereira, C. J. C. Burges, L. Bottou and K. Q. Weinberger, Eds. Montreal, Canada: NIPS, 2012, pp. 2222–30.
-
(2012)
Advances in Neural Information Processing Systems
, vol.25
, pp. 2222-2230
-
-
Srivastava, N.1
Salakhutdinov, R.2
-
60
-
-
78649297301
-
Deep belief networks for phone recognition
-
Vancouver: 2009, and, in
-
A. Mohamed, G. Dahl, and G. Hinton, “Deep belief networks for phone recognition,” in Proceedings of Neural Information Processing Systems 2009 Workshop on Deep Learning for Speech Recognition and Related Applications, Vancouver, 2009.
-
Proceedings of Neural Information Processing Systems 2009 Workshop on Deep Learning for Speech Recognition and Related Applications
-
-
Mohamed, A.1
Dahl, G.2
Hinton, G.3
-
61
-
-
84055212007
-
Sparse multilayer perceptron for phoneme recognition
-
Jan., and
-
G. Sivaram, and H. Hermansky, “Sparse multilayer perceptron for phoneme recognition,”IEEE Trans. Audio, Speech, & Language Processing, Vol. 20, no. 1, pp. 23–9, Jan. 2012.
-
(2012)
IEEE Trans. Audio, Speech, & Language Processing
, vol.20
, Issue.1
, pp. 23-29
-
-
Sivaram, G.1
Hermansky, H.2
-
62
-
-
84055211743
-
Acoustic Modeling Using Deep Belief Networks
-
Jan., and
-
A. Mohamed, G. Dahl, and G. Hinton, “Acoustic Modeling Using Deep Belief Networks,” IEEE Trans. Audio, Speech, & Language Processing, Vol. 20, no. 1, pp. 14–22, Jan. 2012.
-
(2012)
IEEE Trans. Audio, Speech, & Language Processing
, vol.20
, Issue.1
, pp. 14-22
-
-
Mohamed, A.1
Dahl, G.2
Hinton, G.3
-
63
-
-
84867585919
-
Understanding how deep belief networks perform acoustic modelling
-
Kyoto: 2012, and
-
A. Mohamed, G. Hinton, and G. Penn, “Understanding how deep belief networks perform acoustic modelling,” in Proceedings of the 37th International Conference on Acoustics, Speech, and Signal Processing, Kyoto, 2012, pp. 4273–76.
-
Proceedings of the 37th International Conference on Acoustics, Speech, and Signal Processing
, pp. 4273-4276
-
-
Mohamed, A.1
Hinton, G.2
Penn, G.3
-
64
-
-
79959840616
-
Investigation of full-sequence training of deep belief networks for speech recognition
-
Makuhari: 2010, and
-
A. Mohamed, D. Yu, and L. Deng, “Investigation of full-sequence training of deep belief networks for speech recognition,” in Proceedings of the 11th Annual Conference of the International Speech Communication Association, Makuhari, 2010, pp. 2846–9.
-
Proceedings of the 11th Annual Conference of the International Speech Communication Association
, pp. 2846-2849
-
-
Mohamed, A.1
Yu, D.2
Deng, L.3
-
65
-
-
84867606668
-
Exploiting sparseness in deep neural networks for large vocabulary speech recognition
-
Kyoto: 2012, and
-
D. Yu, F. Seide, G. Li, and L. Deng, “Exploiting sparseness in deep neural networks for large vocabulary speech recognition,” in Proceedings of the 37th International Conference on Acoustics, Speech, and Signal Processing, Kyoto, 2012, pp. 4409–12.
-
Proceedings of the 37th International Conference on Acoustics, Speech, and Signal Processing
, pp. 4409-4412
-
-
Yu, D.1
Seide, F.2
Li, G.3
Deng, L.4
-
66
-
-
78049409409
-
Language recognition using deep-structured conditional random fields
-
2010, and
-
D. Yu, S. Wang, Z. Karam, and L. Deng, “Language recognition using deep-structured conditional random fields,” in Proceedings of the 35th International Conference on Acoustics, Speech and Signal Processing, 2010, pp. 5030–3.
-
Proceedings of the 35th International Conference on Acoustics, Speech and Signal Processing
, pp. 5030-5033
-
-
Yu, D.1
Wang, S.2
Karam, Z.3
Deng, L.4
-
67
-
-
84858976070
-
Feature engineering in context-dependent deep neural networks for conversational speech transcription
-
Hawaii: 2011, and
-
F. Seide, G. Li, X. Chen, and D. Yu, “Feature engineering in context-dependent deep neural networks for conversational speech transcription,” in Proceedings of the 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, Hawaii, 2011, pp. 24–9.
-
Proceedings of the 2011 IEEE Workshop on Automatic Speech Recognition and Understanding
, pp. 24-29
-
-
Seide, F.1
Li, G.2
Chen, X.3
Yu, D.4
-
68
-
-
80051616844
-
Context-dependent DBN-HMMs in large vocabulary continuous speech recognition
-
Prague: 2011, and
-
G. Dahl, D. Yu, L. Deng, and A. Acero, “Context-dependent DBN-HMMs in large vocabulary continuous speech recognition,” in Proceedings of the 36th International Conference on Acoustics, Speech, and Signal Processing, Prague, 2011, pp. 4688–91.
-
Proceedings of the 36th International Conference on Acoustics, Speech, and Signal Processing
, pp. 4688-4691
-
-
Dahl, G.1
Yu, D.2
Deng, L.3
Acero, A.4
-
69
-
-
84055222005
-
Context-dependent, pre-trained deep neural networks for large vocabulary speech recognition
-
Jan., and
-
G. Dahl, D. Yu, L. Deng, and A. Acero, “Context-dependent, pre-trained deep neural networks for large vocabulary speech recognition,” IEEE Trans. Audio, Speech, & Language Proc., Vol. 20, no. 1, pp. 30–42, Jan. 2012.
-
(2012)
IEEE Trans. Audio, Speech, & Language Proc
, vol.20
, Issue.1
, pp. 30-42
-
-
Dahl, G.1
Yu, D.2
Deng, L.3
Acero, A.4
-
70
-
-
84878534913
-
Integrating deep neural networks into structural classification approach based on weighted finite-state transducers
-
Portland: 2012, and, in
-
Y. Kubo, T. Hori, and A. Nakamura, “Integrating deep neural networks into structural classification approach based on weighted finite-state transducers,” in Proceedings of the 13th Annual Conference of the International Speech Communication Association, Portland, 2012.
-
Proceedings of the 13th Annual Conference of the International Speech Communication Association
-
-
Kubo, Y.1
Hori, T.2
Nakamura, A.3
-
71
-
-
84890491198
-
Recent advances in deep learning for speech research at Microsoft
-
Vancouver: 2013, and
-
L. Deng, J. Li, K. Huang, D. Yao, F. Yu, M. Seide, G. Seltzer, X. Zweig, J. He, Y. Williams, and A. Acero. “Recent advances in deep learning for speech research at Microsoft,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing, Vancouver, 2013, pp. 8604–8.
-
Proceedings of International Conference on Acoustics, Speech and Signal Processing
, pp. 8604-8608
-
-
Deng, L.1
Li, J.2
Huang, K.3
Yao, D.4
Yu, F.5
Seide, M.6
Seltzer, G.7
Zweig, X.8
He, J.9
Williams, Y.10
Acero, A.11
-
72
-
-
79961219393
-
Discovering binary codes for documents by learning deep generative models
-
Jan., and
-
G. Hinton, and R. Salakhutdinov, “Discovering binary codes for documents by learning deep generative models,” Topics in Cognitive Science, Vol. 3, no. 1, pp. 74–91, Jan. 2011.
-
(2011)
Topics in Cognitive Science
, vol.3
, Issue.1
, pp. 74-91
-
-
Hinton, G.1
Salakhutdinov, R.2
-
73
-
-
67449128732
-
Semantic hashing
-
Jul., and
-
R. Salakhutdinov, and G. Hinton, “Semantic hashing,” International Journal of Approximate Reasoning, Vol. 50, no. 7, pp. 969–78, Jul. 2009.
-
(2009)
International Journal of Approximate Reasoning
, vol.50
, Issue.7
, pp. 969-978
-
-
Salakhutdinov, R.1
Hinton, G.2
-
74
-
-
84888183490
-
Modeling documents with deep Boltzmann machines
-
Bellevue: 2013, and
-
N. Srivastava, R. Salakhutdinov, and G. E. Hinton, “Modeling documents with deep Boltzmann machines,” in Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, Bellevue, 2013, pp. 616–24.
-
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence
, pp. 616-624
-
-
Srivastava, N.1
Salakhutdinov, R.2
Hinton, G.E.3
-
75
-
-
84872431330
-
View of MapReduce: Programming model, methods, and its applications
-
Sept., and
-
W. Fang, W. Pan, and Z. Cui, “View of MapReduce: Programming model, methods, and its applications”, IETE Technical Review, Vol. 29, no. 5, pp. 380–7, Sept. 2012.
-
(2012)
IETE Technical Review
, vol.29
, Issue.5
, pp. 380-387
-
-
Fang, W.1
Pan, W.2
Cui, Z.3
|