-
2
-
-
84857819132
-
Theano: A CPU and GPU math expression compiler
-
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., and Bengio, Y. (2010). Theano: a CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy).
-
(2010)
Proceedings of the Python for Scientific Computing Conference (SciPy)
-
-
Bergstra, J.1
Breuleux, O.2
Bastien, F.3
Lamblin, P.4
Pascanu, R.5
Desjardins, G.6
Turian, J.7
Warde-Farley, D.8
Bengio, Y.9
-
5
-
-
10944265561
-
Helmholtz machines and wake-sleep learning
-
MIT Press, Cambridge, MA
-
Dayan, P. (2000). Helmholtz machines and wake-sleep learning. Handbook of Brain Theory and Neural Network. MIT Press, Cambridge, MA.
-
(2000)
Handbook of Brain Theory and Neural Network
-
-
Dayan, P.1
-
13
-
-
84878919168
-
Stochastic variational inference
-
Hoffman, M., Blei, D., Wang, C, and Paisley, J. (2013). Stochastic variational inference. Journal of Machine Learning Research, 14 (1303-1347).
-
(2013)
Journal of Machine Learning Research
, vol.14
, pp. 1303-1347
-
-
Hoffman, M.1
Blei, D.2
Wang, C.3
Paisley, J.4
-
14
-
-
0001837853
-
Improving the mean field approximation via the use of mixture distributions
-
Springer Netherlands, Dordrecht
-
Jaakkola, T. S. and Jordan, M. I. (1998). Improving the Mean Field Approximation Via the Use of Mixture Distributions. In Learning in Graphical Models, pages 163-173. Springer Netherlands, Dordrecht.
-
(1998)
Learning in Graphical Models
, pp. 163-173
-
-
Jaakkola, T.S.1
Jordan, M.I.2
-
15
-
-
0033225865
-
Introduction to variational methods for graphical models
-
Jordan, M., Ghahramani, Z., Jaakkola, T., and Saul, L. (1999). Introduction to variational methods for graphical models. Machine Learning, 37:183-233.
-
(1999)
Machine Learning
, vol.37
, pp. 183-233
-
-
Jordan, M.1
Ghahramani, Z.2
Jaakkola, T.3
Saul, L.4
-
17
-
-
84997780122
-
-
arXiv preprint arXiv:1603.00788
-
Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., and Blei, D. M. (2016). Automatic differentiation variational inference. arXiv preprint arXiv:1603.00788.
-
(2016)
Automatic Differentiation Variational Inference
-
-
Kucukelbir, A.1
Tran, D.2
Ranganath, R.3
Gelman, A.4
Blei, D.M.5
-
19
-
-
84997812859
-
-
arXiv preprint arXiv: 1602.05473
-
Maaløe, L., Sønderby, C. K., Sønderby, S. K., and Winther, O. (2016). Auxiliary deep generative models. arXiv preprint arXiv: 1602.05473.
-
(2016)
Auxiliary Deep Generative Models
-
-
Maaløe, L.1
Sønderby, C.K.2
Sønderby, S.K.3
Winther, O.4
-
21
-
-
4243447828
-
-
Technical Repport, Department of Computer Science, University of Toronto
-
Neal, R. (1990). Learning stochastic feedforward networks. Technical Repport CRG-TR-90-7: Department of Computer Science, University of Toronto.
-
(1990)
Learning Stochastic Feedforward Networks
-
-
Neal, R.1
-
24
-
-
84995512444
-
Deep exponential families
-
Ranganath, R., Tang, L., Charlin, L., and Blei, D. M. (2015). Deep exponential families. In Artificial Intelligence and Statistics.
-
(2015)
Artificial Intelligence and Statistics
-
-
Ranganath, R.1
Tang, L.2
Charlin, L.3
Blei, D.M.4
-
28
-
-
84891700107
-
Fixed-form variational posterior approximation through stochastic linear regression
-
Salimans, T., Knowles, D. A., et al. (2013). Fixed-form variational posterior approximation through stochastic linear regression. Bayesian Analysis, 8(4):837-882.
-
(2013)
Bayesian Analysis
, vol.8
, Issue.4
, pp. 837-882
-
-
Salimans, T.1
Knowles, D.A.2
-
33
-
-
84965132073
-
Local expectation gradients for doubly stochastic variational inference
-
Titsias, M. K. (2015). Local expectation gradients for doubly stochastic variational inference. In Neural Information Processing Systems.
-
(2015)
Neural Information Processing Systems
-
-
Titsias, M.K.1
-
35
-
-
84997848955
-
-
Tran, D., Blei, D. M., Kucukelbir, A., Dieng, A., Rudolph, M., and Liang, D. (2016a). Edward: A library for probabilistic modeling, inference, and criticism.
-
(2016)
Edward: A Library for Probabilistic Modeling, Inference, and Criticism
-
-
Tran, D.1
Blei, D.M.2
Kucukelbir, A.3
Dieng, A.4
Rudolph, M.5
Liang, D.6
-
37
-
-
65749118363
-
Graphical models, exponential families, and variational inference
-
Wainwright, M. and Jordan, M. (2008). Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1(1-2):1-305.
-
(2008)
Foundations and Trends in Machine Learning
, vol.1
, Issue.1-2
, pp. 1-305
-
-
Wainwright, M.1
Jordan, M.2
-
38
-
-
71149089356
-
Evaluation methods for topic models
-
Wallach, H., Murray, I., Salakhutdinov, R., and Mimno, D. (2009). Evaluation methods for topic models. In International Conference on Machine Learning.
-
(2009)
International Conference on Machine Learning
-
-
Wallach, H.1
Murray, I.2
Salakhutdinov, R.3
Mimno, D.4
|