-
1
-
-
0014841634
-
Multi-variate probit analysis
-
JR Ashford and RR Sowden. Multi-variate probit analysis. Biometrics, pages 535-546, 1970.
-
(1970)
Biometrics
, pp. 535-546
-
-
Ashford, J.R.1
Sowden, R.R.2
-
2
-
-
34247105853
-
Thurstonian-based analyses: Past, present, and future utilities
-
U. Böckenholt. Thurstonian-based analyses: past, present, and future utilities. Psychometrika, 71(4):615-629, 2006.
-
(2006)
Psychometrika
, vol.71
, Issue.4
, pp. 615-629
-
-
Böckenholt, U.1
-
3
-
-
74549208546
-
Expected reciprocal rank for graded relevance
-
ACM
-
O. Chapelle, D. Metlzer, Y. Zhang, and P. Grinspan. Expected reciprocal rank for graded relevance. In CIKM, pages 621-630. ACM, 2009.
-
(2009)
CIKM
, pp. 621-630
-
-
Chapelle, O.1
Metlzer, D.2
Zhang, Y.3
Grinspan, P.4
-
4
-
-
0000911137
-
Analysis of multivariate probit models
-
S. Chib and E. Greenberg. Analysis of multivariate probit models. Biometrika, 85(2):347-361, 1998.
-
(1998)
Biometrika
, vol.85
, Issue.2
, pp. 347-361
-
-
Chib, S.1
Greenberg, E.2
-
6
-
-
21644435901
-
Bayesian latent variable models for mixed discrete outcomes
-
D. B. Dunson and A.H. Herring. Bayesian latent variable models for mixed discrete outcomes. Biostatistics, 6(1): 11, 2005.
-
(2005)
Biostatistics
, vol.6
, Issue.1
, pp. 11
-
-
Dunson, D.B.1
Herring, A.H.2
-
7
-
-
0345368881
-
Unsupervised learning of distributions on binary vectors using two layer networks
-
Y. Freund and D. Haussler. Unsupervised learning of distributions on binary vectors using two layer networks. Advances in Neural Information Processing Systems, pages 912-919, 1993.
-
(1993)
Advances in Neural Information Processing Systems
, pp. 912-919
-
-
Freund, Y.1
Haussler, D.2
-
8
-
-
33749243771
-
The rate adapting Poisson model for information retrieval and object recognition
-
P. V. Gehler, A.D. Holub, and M. Welling. The rate adapting Poisson model for information retrieval and object recognition. In Proceedings of the ICML, pages 337-344, 2006.
-
(2006)
Proceedings of the ICML
, pp. 337-344
-
-
Gehler, P.V.1
Holub, A.D.2
Welling, M.3
-
9
-
-
0001878857
-
Efficient simulation from the multivariate normal and student-t distributions subject to linear constraints and the evaluation of constraint probabilities
-
J. Geweke. Efficient simulation from the multivariate normal and student-t distributions subject to linear constraints and the evaluation of constraint probabilities. In Computing science and statistics: Proceedings of the 23rd symposium on the interface, pages 571-578, 1991.
-
(1991)
Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface
, pp. 571-578
-
-
Geweke, J.1
-
10
-
-
33746600649
-
Reducing the dimensionality of data with neural networks
-
DOI 10.1126/science.1127647
-
G.E. Hinton and R.R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313 (5786):504-507, 2006. (Pubitemid 44148451)
-
(2006)
Science
, vol.313
, Issue.5786
, pp. 504-507
-
-
Hinton, G.E.1
Salakhutdinov, R.R.2
-
13
-
-
26644473169
-
Nonparametric Bayesian modeling for multivariate ordinal data
-
DOI 10.1198/106186005X63185
-
A. Kottas, P. Müller, and F. Quintana. Nonparametric Bayesian modeling for multivariate ordinal data. Journal of Computational and Graphical Statistics, 14(3):610-625, 2005. (Pubitemid 41442152)
-
(2005)
Journal of Computational and Graphical Statistics
, vol.14
, Issue.3
, pp. 610-625
-
-
Kottas, A.1
Muller, P.2
Quintana, F.3
-
14
-
-
79951571992
-
Learning a generative model of images by factoring appearance and shape
-
N. Le Roux, N. Heess, J. Shotton, and J. Winn. Learning a generative model of images by factoring appearance and shape. Neural Computation, 23(3):593-650, 2011.
-
(2011)
Neural Computation
, vol.23
, Issue.3
, pp. 593-650
-
-
Le Roux, N.1
Heess, N.2
Shotton, J.3
Winn, J.4
-
15
-
-
80053437179
-
Multimodal deep learning
-
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A.Y. Ng. Multimodal deep learning. In ICML, 2011.
-
(2011)
ICML
-
-
Ngiam, J.1
Khosla, A.2
Kim, M.3
Nam, J.4
Lee, H.5
Ng, A.Y.6
-
16
-
-
77955989954
-
Modeling pixel means and covariances using factorized third-order Boltzmann machines
-
IEEE
-
M.A. Ranzato and G.E. Hinton. Modeling pixel means and covariances using factorized third-order Boltzmann machines. In CVPR, pages 2551-2558. IEEE, 2010.
-
(2010)
CVPR
, pp. 2551-2558
-
-
Ranzato, M.A.1
Hinton, G.E.2
-
17
-
-
0001153986
-
Simulation of truncated normal variables
-
C.P. Robert. Simulation of truncated normal variables. Statistics and computing, 5(2):121-125, 1995.
-
(1995)
Statistics and Computing
, vol.5
, Issue.2
, pp. 121-125
-
-
Robert, C.P.1
-
21
-
-
78649978910
-
List-wise learning to rank with matrix factorization for collaborative filtering
-
ACM
-
Y. Shi, M. Larson, and A. Hanjalic. List-wise learning to rank with matrix factorization for collaborative filtering. In ACM RecSys, pages 269-272. ACM, 2010.
-
(2010)
ACM RecSys
, pp. 269-272
-
-
Shi, Y.1
Larson, M.2
Hanjalic, A.3
-
23
-
-
84877724347
-
Multimodal learning with deep Boltzmann machines
-
N. Srivastava and R. Salakhutdinov. Multimodal learning with deep Boltzmann machines. In NIPS, pages 2231-2239, 2012.
-
(2012)
NIPS
, pp. 2231-2239
-
-
Srivastava, N.1
Salakhutdinov, R.2
-
24
-
-
58149426837
-
A law of comparative judgment
-
L. L. Thurstone. A law of comparative judgment. Psycho-logical review, 34(4):273, 1927.
-
(1927)
Psycho-logical Review
, vol.34
, Issue.4
, pp. 273
-
-
Thurstone, L.L.1
-
25
-
-
56449086223
-
Training restricted Boltzmann machines using approximations to the likelihood gradient
-
T. Tieleman. Training restricted Boltzmann machines using approximations to the likelihood gradient. In Proceedings of the 25th ICML, pages 1064-1071, 2008.
-
(2008)
Proceedings of the 25th ICML
, pp. 1064-1071
-
-
Tieleman, T.1
-
27
-
-
84867653036
-
Mixed-variate restricted Boltzmann machines
-
T. Tran, D.Q. Phung, and S. Venkatesh. Mixed-variate restricted Boltzmann machines. In Proc. of 3rd Asian Conference on Machine Learning (ACML), Taoyuan, Taiwan, 2011.
-
Proc. of 3rd Asian Conference on Machine Learning (ACML), Taoyuan, Taiwan, 2011
-
-
Tran, T.1
Phung, D.Q.2
Venkatesh, S.3
-
29
-
-
84880115759
-
Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering
-
SIAM
-
T. Truyen, D.Q Phung, and S. Venkatesh. Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering. In Proc. of SIAM Conference on Data Mining (SDM), Mesa, Arizona, USA, 2011. SIAM.
-
Proc. of SIAM Conference on Data Mining (SDM), Mesa, Arizona, USA, 2011
-
-
Truyen, T.1
Phung, D.Q.2
Venkatesh, S.3
-
30
-
-
80053145987
-
Ordinal Boltzmann machines for collaborative filtering
-
T.T. Truyen, D.Q. Phung, and S. Venkatesh. Ordinal Boltzmann machines for collaborative filtering. In Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI), Montreal, Canada, June 2009.
-
Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI), Montreal, Canada, June 2009
-
-
Truyen, T.T.1
Phung, D.Q.2
Venkatesh, S.3
-
32
-
-
0035543522
-
Factor analysis with (mixed) observed and latent variables in the exponential family
-
M. Wedel and W.A. Kamakura. Factor analysis with (mixed) observed and latent variables in the exponential family. Psychometrika, 66(4):515-530, 2001. (Pubitemid 33570085)
-
(2001)
Psychometrika
, vol.66
, Issue.4
, pp. 515-530
-
-
Wedel, M.1
Kamakura, W.A.2
-
34
-
-
84897525607
-
Boltzmann machines with bounded continuous random variables
-
M. Yasuda and K. Tanaka. Boltzmann machines with bounded continuous random variables. Interdisciplinary Information Sciences, 13(1):25-31, 2007.
-
(2007)
Interdisciplinary Information Sciences
, vol.13
, Issue.1
, pp. 25-31
-
-
Yasuda, M.1
Tanaka, K.2
-
35
-
-
0000355193
-
Parametric inference for imperfectly observed Gibbsian fields
-
L. Younes. Parametric inference for imperfectly observed Gibbsian fields. Probability Theory and Related Fields, 82 (4): 625-645, 1989.
-
(1989)
Probability Theory and Related Fields
, vol.82
, Issue.4
, pp. 625-645
-
-
Younes, L.1
-
36
-
-
40249113236
-
Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models
-
X. Zhang, W.J. Boscardin, and T.R. Belin. Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models. Computational statistics & data analysis, 52(7):3697-3708, 2008.
-
(2008)
Computational Statistics & Data Analysis
, vol.52
, Issue.7
, pp. 3697-3708
-
-
Zhang, X.1
Boscardin, W.J.2
Belin, T.R.3
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