-
2
-
-
84897544737
-
Theano: New features and speed improvements
-
F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio. Theano: new features and speed improvements. In Deep Learning and Unsupervised Feature Learning NIPS Workshop, 2012.
-
(2012)
Deep Learning and Unsupervised Feature Learning NIPS Workshop
-
-
Bastien, F.1
Lamblin, P.2
Pascanu, R.3
Bergstra, J.4
Goodfellow, I.5
Bergeron, A.6
Bouchard, N.7
Warde-Farley, D.8
Bengio, Y.9
-
3
-
-
84919906761
-
Deep generative stochastic networks trainable by backprop
-
Y. Bengio, E. Laufer, G. Alain, and J. Yosinski. Deep generative stochastic networks trainable by backprop. In ICML, 2014.
-
(2014)
ICML
-
-
Bengio, Y.1
Laufer, E.2
Alain, G.3
Yosinski, J.4
-
4
-
-
84867776237
-
Large-margin predictive latent subspace learning for multi-view data analysis
-
N. Chen, J. Zhu, F. Sun, and E. P. Xing. Large-margin predictive latent subspace learning for multi-view data analysis. IEEE Trans. on PAMI, 34(12):2365-2378, 2012.
-
(2012)
IEEE Trans. on PAMI
, vol.34
, Issue.12
, pp. 2365-2378
-
-
Chen, N.1
Zhu, J.2
Sun, F.3
Xing, E.P.4
-
7
-
-
84937849144
-
Generative adversarial nets
-
I. J. Goodfellow, J. P. Abadie, M. Mirza, B. Xu, D. W. Farley, S.ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, 2014.
-
(2014)
NIPS
-
-
Goodfellow, I.J.1
Abadie, J.P.2
Mirza, M.3
Xu, B.4
Farley, D.W.5
Ozair, S.6
Courville, A.7
Bengio, Y.8
-
8
-
-
84897543523
-
Maxout networks
-
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. C. Courville, and Y. Bengio. Maxout networks. In ICML, 2013.
-
(2013)
ICML
-
-
Goodfellow, I.J.1
Warde-Farley, D.2
Mirza, M.3
Courville, A.C.4
Bengio, Y.5
-
9
-
-
84919796355
-
Deep autoregressive networks
-
K. Gregor, I. Danihelka, A. Mnih, C. Blundell, and D. Wierstra. Deep autoregressive networks. In ICML, 2014.
-
(2014)
ICML
-
-
Gregor, K.1
Danihelka, I.2
Mnih, A.3
Blundell, C.4
Wierstra, D.5
-
10
-
-
85083951076
-
A method for stochastic optimization
-
D. P. Kingma and J. L. Ba. Adam: A method for stochastic optimization. In ICLR, 2015.
-
(2015)
ICLR
-
-
Kingma, D.P.1
Adam, J.L.Ba.2
-
12
-
-
85083952489
-
Auto-encoding variational Bayes
-
D. P. Kingma and M. Welling. Auto-encoding variational Bayes. In ICLR, 2014.
-
(2014)
ICLR
-
-
Kingma, D.P.1
Welling, M.2
-
13
-
-
84862524901
-
The neural autoregressive distribution estimator
-
H. Larochelle and I. Murray. The neural autoregressive distribution estimator. In AISTATS, 2011.
-
(2011)
AISTATS
-
-
Larochelle, H.1
Murray, I.2
-
15
-
-
85009928594
-
Deeply-supervised nets
-
C. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu. Deeply-supervised nets. In AISTATS, 2015.
-
(2015)
AISTATS
-
-
Lee, C.1
Xie, S.2
Gallagher, P.3
Zhang, Z.4
Tu, Z.5
-
16
-
-
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
-
18
-
-
84965120402
-
Statistical analysis with missing data
-
R. J. Little and D. B. Rubin. Statistical analysis with missing data. JMLR, 539, 1987.
-
(1987)
JMLR
, vol.539
-
-
Little, R.J.1
Rubin, D.B.2
-
19
-
-
57249084011
-
Visualizing data using t-SNE
-
L. V. Matten and G. Hinton. Visualizing data using t-SNE. JMLR, 9:2579-2605, 2008.
-
(2008)
JMLR
, vol.9
, pp. 2579-2605
-
-
Matten, L.V.1
Hinton, G.2
-
20
-
-
84867135428
-
Max-margin min-entropy models
-
K. Miller, M. P. Kumar, B. Packer, D. Goodman, and D. Koller. Max-margin min-entropy models. In AISTATS, 2012.
-
(2012)
AISTATS
-
-
Miller, K.1
Kumar, M.P.2
Packer, B.3
Goodman, D.4
Koller, D.5
-
21
-
-
84919786239
-
Neural variational inference and learning in belief networks
-
A. Mnih and K. Gregor. Neural variational inference and learning in belief networks. In ICML, 2014.
-
(2014)
ICML
-
-
Mnih, A.1
Gregor, K.2
-
22
-
-
84865114495
-
Reading digits in natural images with unsupervised feature learning
-
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng. Reading digits in natural images with unsupervised feature learning. NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.
-
(2011)
NIPS Workshop on Deep Learning and Unsupervised Feature Learning
-
-
Netzer, Y.1
Wang, T.2
Coates, A.3
Bissacco, A.4
Wu, B.5
Ng, A.Y.6
-
23
-
-
80052877144
-
On deep generative models with applications to recognition
-
M. Ranzato, J. Susskind, V. Mnih, and G. E. Hinton. On deep generative models with applications to recognition. In CVPR, 2011.
-
(2011)
CVPR
-
-
Ranzato, M.1
Susskind, J.2
Mnih, V.3
Hinton, G.E.4
-
24
-
-
84919796093
-
Stochastic backpropagation and approximate inference in deep generative models
-
D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In ICML, 2014.
-
(2014)
ICML
-
-
Rezende, D.J.1
Mohamed, S.2
Wierstra, D.3
-
27
-
-
84874575248
-
Convolutional neural networks applied to house numbers digit classification
-
P. Sermanet, S. Chintala, and Y. Lecun. Convolutional neural networks applied to house numbers digit classification. In ICPR, 2012.
-
(2012)
ICPR
-
-
Sermanet, P.1
Chintala, S.2
Lecun, Y.3
-
28
-
-
79952748054
-
Pegasos: Primal estimated sub-gradient solver for SVM
-
S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter. Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, Series B, 2011.
-
(2011)
Mathematical Programming, Series B
-
-
Shalev-Shwartz, S.1
Singer, Y.2
Srebro, N.3
Cotter, A.4
-
31
-
-
14344250451
-
Support vector machine learning for interdependent and structured output spaces
-
I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In ICML, 2004.
-
(2004)
ICML
-
-
Tsochantaridis, I.1
Hofmann, T.2
Joachims, T.3
Altun, Y.4
-
32
-
-
71149086466
-
Learning structural SVMs with latent variables
-
C. J. Yu and T. Joachims. Learning structural SVMs with latent variables. In ICML, 2009.
-
(2009)
ICML
-
-
Yu, C.J.1
Joachims, T.2
-
33
-
-
85083954484
-
Stochastic pooling for regularization of deep convolutional neural networks
-
M. D. Zeiler and R. Fergus. Stochastic pooling for regularization of deep convolutional neural networks. In ICLR, 2013.
-
(2013)
ICLR
-
-
Zeiler, M.D.1
Fergus, R.2
-
34
-
-
84869186087
-
MedLDA: Maximum margin supervised topic models
-
J. Zhu, A. Ahmed, and E. P. Xing. MedLDA: Maximum margin supervised topic models. JMLR, 13:2237-2278, 2012.
-
(2012)
JMLR
, vol.13
, pp. 2237-2278
-
-
Zhu, J.1
Ahmed, A.2
Xing, E.P.3
-
35
-
-
84899824631
-
Gibbs max-margin topic models with data augmentation
-
J. Zhu, N. Chen, H. Perkins, and B. Zhang. Gibbs max-margin topic models with data augmentation. JMLR, 15:1073-1110, 2014.
-
(2014)
JMLR
, vol.15
, pp. 1073-1110
-
-
Zhu, J.1
Chen, N.2
Perkins, H.3
Zhang, B.4
-
36
-
-
84902818267
-
Bayesian inference with posterior regularization and applications to infinite latent SVMs
-
J. Zhu, N. Chen, and E. P. Xing. Bayesian inference with posterior regularization and applications to infinite latent SVMs. JMLR, 15:1799-1847, 2014.
-
(2014)
JMLR
, vol.15
, pp. 1799-1847
-
-
Zhu, J.1
Chen, N.2
Xing, E.P.3
-
37
-
-
85162025905
-
Partially observed maximum entropy discrimination Markov networks
-
J. Zhu, E. P. Xing, and B. Zhang. Partially observed maximum entropy discrimination Markov networks. In NIPS, 2008.
-
(2008)
NIPS
-
-
Zhu, J.1
Xing, E.P.2
Zhang, B.3
|