Variational generative stochastic networks with collaborative shaping

32nd International Conference on Machine Learning, ICML 2015

Volumn 3, Issue , 2015, Pages 1964-1972

  Bachman, Philip ^a   Precup, Doina ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84970024016     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bengio, Yoshua, Yao, Li, Alain, Guillaume, and Vincent, Pascal. Generalized denoising auto-encoders as generative models. In Advances in Neural Information Processing Systems (NIPS), 2013.
2. Bengio, Yoshua, Thibodeau-Laufer, Éric, Alain, Guillaume, and Yosinski, Jason. Deep generative stochastic networks trainable by backprop. arXiv:1306.1091v5 [cs.LG], 2014.
3. Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., and Bengio, Y. Theano: A cpu and gpu math expression compiler. In Python for Scientific Computing Conference (SciPy), 2010.
4. Breuleux, Olivier, Bengio, Yoshua, and Vincent, Pascal. Quickly generating representative samples from an rbm-derived process. Neural Computation, 23(8):2053-2073, 2011.
5. Goodfellow, Ian J, Warde-Farley, David, Mirza, Mehdi, Courville, Aaron, and Bengio, Yoshua. Maxout networks. In International Conference on Machine Learning (ICML), 2013.
6. Goodfellow, Ian J, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sheijil, Courville, Aaron, and Bengio, Yoshua. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS), 2014.
7. Gutmann, Michael U, Dutta, Ritabrata, Kaski, Samuel, and Corander, Jukka. Classifier abc. In MCMSki IV (posters), 2014a.
8. Gutmann, Michael U, Dutta, Ritabrata, Kaski, Samuel, and Corander, Jukka. Likelihood-free inference via classification. In arXiv: 1407.498lvl [stat.CO], 2014b.
9. Hastie, Trevor, Friedman, Jerome, and Tibshirani, Robert. Elements of Statistical Learning II. 2008.
10. Kingma, Diederik P and Ba, Jimmy. Adam: A method for stochastic optimization. arXiv: 1412.6980v2 [cs.LG], 2015.
11. Kingma, Diederik P and Welling, Max. Auto-encoding variational bayes. In International Conference on Learning Representations (ICLR), 2014.
12. Levine, Sergey and Koltun, Vladen. Guided policy search. In International Conference on Machine Learning (ICML), 2013.
13. Levine, Sergey, Wagener, Nolan, and Abbeel, Pieter. Learning contact-rich manipulation skills with guided policy search. In ICRA, 2015.
14. Mnih, Andriy and Gregor, Karol. Neural variational inference and learning. In International Conference on Machine Learning (ICML), 2014.
15. Peters, Jan, Mulling, Karen, and Altun, Yasemin. Relative entropy policy search. In AAAI, 2010.
16. Rezende, Danilo, Mohamed, Shakir, and Wierstra, Daan. Stochastic backpropagation and approximate inference in deep generative models. In International Conference on Machine Learning (ICML), 2014.
17. Todorov, Emanuel. Efficient computation of optimal action. Proceedings of the National Academy of Science (PNAS), 106(18): 11478-11483, 2009.
18. Zou, Hui and Hastie, Trevor. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, B., 67(2):301-320, 2005.