Y. Bengio and Y. Lecun, Scaling learning algorithms towards AI, Large Scale Kernel Machines, 2007.

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative adversarial nets, Advances in neural information processing systems, pp.2672-2680, 2014.

G. E. Hinton, S. Osindero, and Y. W. Teh, A Fast Learning Algorithm for Deep Belief Nets, Neural Computation, vol.18, issue.7, pp.1527-1554, 2006.
DOI : 10.1162/jmlr.2003.4.7-8.1235

URL : http://www.cs.berkeley.edu/~ywteh/research/ebm/nc2006.pdf

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998.
DOI : 10.1109/5.726791

A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, Adversarial autoencoders . arXiv preprint, 2015.

A. Marco, . Pimentel, A. David, L. Clifton, L. Clifton et al., A review of novelty detection, Signal Processing, vol.99, pp.215-249, 2014.

S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio, Contractive auto-encoders: Explicit invariance during feature extraction, Proceedings of the 28th international conference on machine learning (ICML-11), pp.833-840, 2011.

I. Sutskever, J. Martens, E. George, G. E. Dahl, and . Hinton, On the importance of initialization and momentum in deep learning, ICML, vol.28, issue.3, pp.1139-1147, 2013.

B. Benjamin, . Thompson, J. Robert, . Marks, J. Jai et al., Implicit learning in autoencoder novelty assessment, Neural Networks, 2002. IJCNN'02. Proceedings of the 2002 International Joint Conference on, pp.2878-2883, 2002.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research, vol.11, pp.3371-3408, 2010.