-
2
-
-
85052634353
-
-
arXiv preprint
-
A. A. Alemi, B. Poole, I. Fischer, J. V. Dillon, R. A. Saurous, and K. Murphy. An information-theoretic analysis of deep latent-variable models. arXiv preprint arXiv:1711.00464, 2017.
-
(2017)
An Information-Theoretic Analysis of Deep Latent-Variable Models
-
-
Alemi, A.A.1
Poole, B.2
Fischer, I.3
Dillon, J.V.4
Saurous, R.A.5
Murphy, K.6
-
5
-
-
84998888548
-
-
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio. Generating sentences from a continuous space. arXiv:1511.06349, 2016.
-
(2016)
Generating Sentences from A Continuous Space
-
-
Bowman, S.R.1
Vilnis, L.2
Vinyals, O.3
Dai, A.M.4
Jozefowicz, R.5
Bengio, S.6
-
7
-
-
85046079137
-
-
X. Chen, D. P. Kingma, T. Salimans, Y. Duan, P. Dhariwal, J. Schulman, I. Sutskever, and P. Abbeel. Variational Lossy Autoencoder. arXiv:1611.02731, 2016.
-
(2016)
Variational Lossy Autoencoder
-
-
Chen, X.1
Kingma, D.P.2
Salimans, T.3
Duan, Y.4
Dhariwal, P.5
Schulman, J.6
Sutskever, I.7
Abbeel, P.8
-
9
-
-
85031918934
-
-
N. Dilokthanakul, P. A. Mediano, M. Garnelo, M. C. Lee, H. Salimbeni, K. Arulkumaran, and M. Shanahan. Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv:1611.02648, 2016.
-
(2016)
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
-
-
Dilokthanakul, N.1
Mediano, P.A.2
Garnelo, M.3
Lee, M.C.4
Salimbeni, H.5
Arulkumaran, K.6
Shanahan, M.7
-
11
-
-
84983185824
-
Training generative neural networks via maximum mean discrepancy optimization
-
G. Dziugaite, D. Roy, and Z. Ghahramani. Training generative neural networks via maximum mean discrepancy optimization. UAI, pages 258–267, 2015.
-
(2015)
UAI
, pp. 258-267
-
-
Dziugaite, G.1
Roy, D.2
Ghahramani, Z.3
-
12
-
-
84862277874
-
Understanding the difficulty of training deep feedforward neural networks
-
X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. AISTATS, 9:249–256, 2010.
-
(2010)
AISTATS
, vol.9
, pp. 249-256
-
-
Glorot, X.1
Bengio, Y.2
-
13
-
-
84937849144
-
Generative adversarial nets
-
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. NIPS, pages 2672–2680, 2014.
-
(2014)
NIPS
, pp. 2672-2680
-
-
Goodfellow, I.1
Pouget-Abadie, J.2
Mirza, M.3
Xu, B.4
Warde-Farley, D.5
Ozair, S.6
Courville, A.7
Bengio, Y.8
-
14
-
-
85029029657
-
-
P. Goyal, Z. Hu, X. Liang, C. Wang, and E. Xing. Nonparametric Variational Autoencoders for Hierarchical Representation Learning. arXiv:1703.07027, 2017.
-
(2017)
Nonparametric Variational Autoencoders for Hierarchical Representation Learning
-
-
Goyal, P.1
Hu, Z.2
Liang, X.3
Wang, C.4
Xing, E.5
-
15
-
-
85047008651
-
-
I. Gulrajani, K. Kumar, F. Ahmed, A. A. Taiga, F. Visin, D. Vazquez, and A. Courville. PixelVAE: A latent variable model for natural images. arXiv:1611.05013, 2016.
-
(2016)
PixelVAE: A Latent Variable Model for Natural Images
-
-
Gulrajani, I.1
Kumar, K.2
Ahmed, F.3
Taiga, A.A.4
Visin, F.5
Vazquez, D.6
Courville, A.7
-
18
-
-
85018920337
-
Improved variational inference with inverse autoregressive flow
-
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling. Improved variational inference with inverse autoregressive flow. NIPS, pages 4743–4751, 2016.
-
(2016)
NIPS
, pp. 4743-4751
-
-
Kingma, D.P.1
Salimans, T.2
Jozefowicz, R.3
Chen, X.4
Sutskever, I.5
Welling, M.6
-
20
-
-
84949683101
-
Human-level concept learning through probabilistic program induction
-
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 350(6266):1332–1338, 2015.
-
(2015)
Science
, vol.350
, Issue.6266
, pp. 1332-1338
-
-
Lake, B.M.1
Salakhutdinov, R.2
Tenenbaum, J.B.3
-
21
-
-
84862524901
-
The neural autoregressive distribution estimator
-
H. Larochelle and I. Murray. The Neural Autoregressive Distribution Estimator. AISTATS, 2011.
-
(2011)
AISTATS
-
-
Larochelle, H.1
Murray, I.2
-
23
-
-
84970016114
-
Generative moment matching networks
-
Y. Li, K. Swersky, and R. S. Zemel. Generative moment matching networks. ICML, pages 1718–1727, 2015.
-
(2015)
ICML
, pp. 1718-1727
-
-
Li, Y.1
Swersky, K.2
Zemel, R.S.3
-
24
-
-
85019242057
-
Rényi Divergence Variational Inference
-
Y. Li and R. E. Turner. Rényi Divergence Variational Inference. NIPS, pages 1073–1081, 2016.
-
(2016)
NIPS
, pp. 1073-1081
-
-
Li, Y.1
Turner, R.E.2
-
26
-
-
84987948522
-
-
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey. Adversarial autoencoders. arXiv:1511.05644, 2015.
-
(2015)
Adversarial Autoencoders
-
-
Makhzani, A.1
Shlens, J.2
Jaitly, N.3
Goodfellow, I.4
Frey, B.5
-
27
-
-
80053455323
-
Inductive principles for restricted boltzmann machine learning
-
B. Marlin, K. Swersky, B. Chen, and N. Freitas. Inductive principles for Restricted Boltzmann Machine learning. AISTATS, pages 509–516, 2010.
-
(2010)
AISTATS
, pp. 509-516
-
-
Marlin, B.1
Swersky, K.2
Chen, B.3
Freitas, N.4
-
28
-
-
85031128630
-
Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks
-
L. Mescheder, S. Nowozin, and A. Geiger. Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks. In ICML, pages 2391–2400, 2017.
-
(2017)
ICML
, pp. 2391-2400
-
-
Mescheder, L.1
Nowozin, S.2
Geiger, A.3
-
31
-
-
85040760064
-
Hierarchical variational models
-
R. Ranganath, D. Tran, and D. Blei. Hierarchical variational models. In ICML, pages 324–333, 2016.
-
(2016)
ICML
, pp. 324-333
-
-
Ranganath, R.1
Tran, D.2
Blei, D.3
-
33
-
-
84919908080
-
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. ICML, pages 1278–1286, 2014.
-
(2014)
ICML
, pp. 1278-1286
-
-
Rezende, D.J.1
Mohamed, S.2
Wierstra, D.3
-
34
-
-
56449102578
-
On the quantitative analysis of deep belief networks
-
R. Salakhutdinov and I. Murray. On the quantitative analysis of deep belief networks. ICML, pages 872–879, 2008.
-
(2008)
ICML
, pp. 872-879
-
-
Salakhutdinov, R.1
Murray, I.2
-
35
-
-
84969835291
-
Markov chain monte carlo and variational inference: Bridging the gap
-
T. Salimans, D. Kingma, and M. Welling. Markov chain monte carlo and variational inference: Bridging the gap. ICML, pages 1218–1226, 2015.
-
(2015)
ICML
, pp. 1218-1226
-
-
Salimans, T.1
Kingma, D.2
Welling, M.3
-
36
-
-
85019264158
-
Ladder variational autoencoders
-
C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, and O. Winther. Ladder variational autoencoders. NIPS, pages 3738–3746, 2016.
-
(2016)
NIPS
, pp. 3738-3746
-
-
Sønderby, C.K.1
Raiko, T.2
Maaløe, L.3
Sønderby, S.K.4
Winther, O.5
-
41
-
-
85018873682
-
Conditional image generation with PixelCNN decoders
-
A. van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves, and K. Kavukcuoglu. Conditional image generation with PixelCNN decoders. NIPS, pages 4790–4798, 2016.
-
(2016)
NIPS
, pp. 4790-4798
-
-
van den Oord, A.1
Kalchbrenner, N.2
Espeholt, L.3
Vinyals, O.4
Graves, A.5
Kavukcuoglu, K.6
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