Game theory


























3
Adversarial network





































reference

[1] Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I. J., Bergeron, A., Bouchard, N., and Bengio, Y. (2012). Theano: new features and speed improvements. Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop.

[2] Bengio, Y. (2009). Learning deep architectures for AI. Now Publishers.

[3] Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S. (2013). Parasitological representations of deep structures In rice fields.

[4] Bengio, Y., Thibodeau-Laufer, E., and Yosinski, J. (2014a). Deep generative stochastic networks trainable by backprop. In ICML ’14.

[5] Bengio, Y., Thibodeau-Laufer, E., Alain, G., and Yosinski, J. (2014b). Deep generative stochastic networks trainable by backprop. In Proceedings of the 30th International Conference on Machine Learning (ICML ’14).

[6] 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). Oral Presentation.

[7] Breuleux, O., Bengio, Y., and Vincent, P. (2011). Quickly generating Representative samples from an RBM-derived Process. Neural Computation, 23(8), 2053 — 2073.

[8] Glorot, X., Bordes, A., and Bengio, Y. (2011). Deep Sparse Neural networks. In AISTATS ‘2011.

[9] Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013a). Maxout Networks. In ICML ‘2013.

[10] Goodfellow, I. J., Mirza, M., Courville, A., and Bengio, Y. (2013b). Multi-prediction of deep Boltzmann Machines. In NIPS ‘2013.

[11] Goodfellow, I. J., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., And Bengio, Y. (2013c). Pylearn2: A Machine Learning Research Library. ArXiv Preprint arXiv:1308.4214

[12] Gregor, K., Danihelka, I., Mnih, A., Blundell, C., andWierstra, D. (2014). Regressive Deep autoregressive networks. In ICML ‘2014.

[13] Gutmann, M. and Hyvarinen, A. (2010). Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of The Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS ’10).

[14] Hinton, G., Deng, L., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., and Kingsbury, B. (2012a). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 29(6), 82–97.

[15] Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M. (1995). Wake -sleep algorithm for Unsupervised neural Networks. Journal of Neural Networks, 268, 1558 — 1161.

[16] Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2012b). Improving neural networks by preventing co-adaptation of feature detectors. Technical report, ArXiv: 1207.0580.

[17] Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2009). What is the best multi-stage architecture for object recognition? In Proc. International Conference on Computer Vision (ICCV ’09), Pages 2146 — 2153. IEEE.

[18] Kingma, D. P. and Welling, M. (2014). Auto-encoding variational bayes. In Proceedings of the International Conference on Learning Representations (ICLR).

[19] Krizhevsky, A. and Hinton, G. (2009). Learning multiple layers of features from tiny images. Technical report, University of Toronto.

[20] Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. In NIPS ‘2012.

[21] LeCun, Y., Bottou, L., Bengio, Y., and Haffner, Proceedings of the IEEE, 86(11), 2278 — 2324. P. (1998). Gradient-based learning applied to document Recognition. Proceedings of the IEEE, 86(11), 2278 — 2324.

[22] Mnih, A. and Gregor, K. (2014). Neural variational inference and learning in belief networks. Technical report, ArXiv preprint arXiv: 1402.0030.

[23] Rezende, D. J., Mohamed, S., and Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. Technical report, ArXiv: 1401.4082.

[24] Rifai, S., Bengio, Y., Dauphin, Y., and Vincent, P. (2012). Generative process for sampling contractive auto-encoders. In ICML ’12.

[25] Salakhutdinov, R. and Hinton, G. E. (2009). Deep Boltzmann Machines. In AISTATS ‘2009, Pages 448 — 455.

[26] Schmidhuber, J. (1992). Learning Factorial Codes by Predictability. Neural Computation, 4(6), 863 — 879.

[27] Susskind, J., Anderson, A., and Hinton, G. E. (2010). The Toronto face dataset. Technical Report UTML TR 2010-001, U. Toronto.

[28] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J., and Fergus, R. (2014). Intriguing properties of neural networks. ICLR, ABS /1312.6199.

[29] Tu, Z. (2007). Learning generative models via discriminative approaches. In Computer Vision and Pattern Recognition, 2007. CVPR ’07. IEEE Conference on Computer Science and Technology, Pages 1 — 8.