-
3
-
-
8644225400
-
Hierarchical topic models and the nested Chinese restaurant process
-
Blei, D. M., Griffiths, T., Jordan, M. I., and Tenenbaum, J. B. Hierarchical topic models and the nested Chinese restaurant process. NIPS, 2004.
-
(2004)
NIPS
-
-
Blei, D.M.1
Griffiths, T.2
Jordan, M.I.3
Tenenbaum, J.B.4
-
5
-
-
84919787787
-
Stochastic gradient hamiltonian monte carlo
-
Chen, T., Fox, E., and Guestrin, C. Stochastic gradient Hamiltonian Monte Carlo. ICML, 2014.
-
(2014)
ICML
-
-
Chen, T.1
Fox, E.2
Guestrin, C.3
-
6
-
-
84919796093
-
Stochastic back-propagation and approximate inference in deep generative models
-
Danilo, J. R., Shakir, M., and Daan, W. Stochastic back-propagation and approximate inference in deep generative models. ICML, 2014.
-
(2014)
ICML
-
-
Danilo, J.R.1
Shakir, M.2
Daan, W.3
-
7
-
-
84937959155
-
Bayesian sampling using stochastic gradient thermostats
-
Ding, N., Fang, Y., Babbush, R., Chen, C., Skeel, R. D., and Neven, H. Bayesian sampling using stochastic gradient thermostats. NIPS, 2014.
-
(2014)
NIPS
-
-
Ding, N.1
Fang, Y.2
Babbush, R.3
Chen, C.4
Skeel, R.D.5
Neven, H.6
-
8
-
-
84965104862
-
Learning deep sigmoid belief networks with data augmentation
-
Gan, Z., Henao, R., Carlson, D., and Carin, L. Learning deep sigmoid belief networks with data augmentation. AISTATS, 2015.
-
(2015)
AISTATS
-
-
Gan, Z.1
Henao, R.2
Carlson, D.3
Carin, L.4
-
9
-
-
79952295497
-
Riemann manifold langevin and hamiltonian monte carlo methods
-
Girolami, M. and Calderhead, B. Riemann manifold Langevin and Hamiltonian Monte Carlo methods. J. R. Statist. Soc. B, 2011.
-
(2011)
J. R. Statist. Soc. B
-
-
Girolami, M.1
Calderhead, B.2
-
11
-
-
0013344078
-
Training products of experts by minimizing contrastive divergence
-
Hinton, G. E. Training products of experts by minimizing contrastive divergence. Neural computation, 2002.
-
(2002)
Neural Computation
-
-
Hinton, G.E.1
-
12
-
-
79961219393
-
Discovering binary codes for documents by learning deep generative models
-
Hinton, G. E. and Salakhutdinov, R. Discovering binary codes for documents by learning deep generative models. Topics in Cognitive Science, 2011.
-
(2011)
Topics in Cognitive Science
-
-
Hinton, G.E.1
Salakhutdinov, R.2
-
14
-
-
84898988368
-
More effective distributed ml via a stale synchronous parallel parameter server
-
Ho, Q., Cipar, J., Cui, H., Kim, J. K., Lee, S., Gibbons, P. B., Gibbons, G. A., Ganger, G. R., and Xing, E. P. More effective distributed ml via a stale synchronous parallel parameter server. NIPS, 2013.
-
(2013)
NIPS
-
-
Ho, Q.1
Cipar, J.2
Cui, H.3
Kim, J.K.4
Lee, S.5
Gibbons, P.B.6
Gibbons, G.A.7
Ganger, G.R.8
Xing, E.P.9
-
15
-
-
85162005069
-
Online learning for latent dirichlet allocation
-
Hoffman, M. D., Blei, D. M., and Bach, F. Online learning for latent Dirichlet allocation. NIPS, 2010.
-
(2010)
NIPS
-
-
Hoffman, M.D.1
Blei, D.M.2
Bach, F.3
-
16
-
-
84878919168
-
Stochastic variational inference
-
Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J. Stochastic variational inference. JMLR, 2013.
-
(2013)
JMLR
-
-
Hoffman, M.D.1
Blei, D.M.2
Wang, C.3
Paisley, J.4
-
17
-
-
85083952489
-
Auto-encoding variational bayes
-
Kingma, D. P. and Welling, M. Auto-encoding variational Bayes. ICLR, 2014.
-
(2014)
ICLR
-
-
Kingma, D.P.1
Welling, M.2
-
18
-
-
84877761544
-
A neural autoregressive topic model
-
Larochelle, H. and Lauly, S. A neural autoregressive topic model. NIPS, 2012.
-
(2012)
NIPS
-
-
Larochelle, H.1
Lauly, S.2
-
19
-
-
84937889303
-
Communication efficient distributed machine learning with the parameter server
-
Li, M., Andersen, D., Smola, A., and Yu, K. Communication efficient distributed machine learning with the parameter server. NIPS, 2014.
-
(2014)
NIPS
-
-
Li, M.1
Andersen, D.2
Smola, A.3
Yu, K.4
-
21
-
-
84867121232
-
Sparse stochastic inference for latent dirichlet allocation
-
Mimno, D., Hoffman, M. D., and Blei, D. M. Sparse stochastic inference for latent Dirichlet allocation. ICML, 2012.
-
(2012)
ICML
-
-
Mimno, D.1
Hoffman, M.D.2
Blei, D.M.3
-
22
-
-
84919786239
-
Neural variational inference and learning in belief networks
-
Mnih, A. and Gregor, K. Neural variational inference and learning in belief networks. ICML, 2014.
-
(2014)
ICML
-
-
Mnih, A.1
Gregor, K.2
-
23
-
-
44049116681
-
Connectionist learning of belief networks
-
Neal, R. M. Connectionist learning of belief networks. Artificial Intelligence, 1992.
-
(1992)
Artificial Intelligence
-
-
Neal, R.M.1
-
24
-
-
84920971064
-
Nested hierarchical dirichlet processes
-
Paisley, J., Wang, C., Blei, D. M., and Jordan, M. I. Nested hierarchical Dirichlet processes. PAMI, 2015.
-
(2015)
PAMI
-
-
Paisley, J.1
Wang, C.2
Blei, D.M.3
Jordan, M.I.4
-
25
-
-
84898939739
-
Stochastic gradient rie-mannian langevin dynamics on the probability simplex
-
Patterson, S. and Teh, Y. W. Stochastic gradient Rie-mannian Langevin dynamics on the probability simplex. NIPS, 2013.
-
(2013)
NIPS
-
-
Patterson, S.1
Teh, Y.W.2
-
26
-
-
84858279402
-
Local shrinkage rules, lévy processes and regularized regression
-
Polson, N. G. and Scott, J. G. Local shrinkage rules, Lévy processes and regularized regression. J. R. Statist. Soc. B, 2012.
-
(2012)
J. R. Statist. Soc. B
-
-
Polson, N.G.1
Scott, J.G.2
-
27
-
-
84884917671
-
Bayesian inference for logistic models using pólya-gamma latent variables
-
Polson, N. G., Scott, J. G., and Windle, J. Bayesian inference for logistic models using Pólya-Gamma latent variables. JASA, 2013.
-
(2013)
JASA
-
-
Polson, N.G.1
Scott, J.G.2
Windle, J.3
-
28
-
-
84965138820
-
Deep exponential families
-
Ranganath, R., Tang, L., Charlin, L., and Blei, D. M. Deep exponential families. AISTATS, 2015.
-
(2015)
AISTATS
-
-
Ranganath, R.1
Tang, L.2
Charlin, L.3
Blei, D.M.4
-
29
-
-
77956556686
-
Replicated softmax: An undirected topic model
-
Salakhutdinov, R. and Hinton, G. E. Replicated softmax: an undirected topic model. NIPS, 2009a.
-
(2009)
NIPS
-
-
Salakhutdinov, R.1
Hinton, G.E.2
-
32
-
-
33749249312
-
Hierarchical dirichlet processes
-
Teh, Y. W., Jordan, M. I., Beal, M. J., and Blei, D. M. Hierarchical Dirichlet processes. JASA, 2006.
-
(2006)
JASA
-
-
Teh, Y.W.1
Jordan, M.I.2
Beal, M.J.3
Blei, D.M.4
-
34
-
-
84877762487
-
Truncation-free stochastic variational inference for Bayesian nonparametric models
-
Wang, C. and Blei, D. M. Truncation-free stochastic variational inference for Bayesian nonparametric models. NIPS, 2012.
-
(2012)
NIPS
-
-
Wang, C.1
Blei, D.M.2
-
35
-
-
80053452150
-
Bayesian learning via stochastic gradient langevin dynamics
-
Welling, M. and Teh, Y. W. Bayesian learning via stochastic gradient Langevin dynamics. ICML, 2011.
-
(2011)
ICML
-
-
Welling, M.1
Teh, Y.W.2
-
36
-
-
77956537774
-
The IBP compound dirichlet process and its application to focused topic modeling
-
Williamson, S., Wang, C., Heller, K., and Blei, D. M. The IBP compound Dirichlet process and its application to focused topic modeling. ICML, 2010.
-
(2010)
ICML
-
-
Williamson, S.1
Wang, C.2
Heller, K.3
Blei, D.M.4
-
37
-
-
84899029362
-
The convergence of contrastive divergences
-
Yuille, A. The convergence of contrastive divergences. NIPS, 2005.
-
(2005)
NIPS
-
-
Yuille, A.1
-
38
-
-
84877756924
-
Joint modeling of a matrix with associated text via latent binary features
-
Zhang, X. and Carin, L. Joint modeling of a matrix with associated text via latent binary features. NIPS, 2012.
-
(2012)
NIPS
-
-
Zhang, X.1
Carin, L.2
-
39
-
-
84920998554
-
Negative binomial process count and mixture modeling
-
Zhou, M. and Carin, L. Negative binomial process count and mixture modeling. PAM1, 2015.
-
(2015)
PAM1
-
-
Zhou, M.1
Carin, L.2
-
40
-
-
84866013303
-
Beta-negative binomial process and poisson factor analysis
-
Zhou, M., Hannah, L., Dunson, D., and Carin, L. Beta-negative binomial process and Poisson factor analysis. AISTATS, 2012.
-
(2012)
AISTATS
-
-
Zhou, M.1
Hannah, L.2
Dunson, D.3
Carin, L.4
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