G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. R. Mohamed et al., Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, IEEE Signal Processing Magazine, vol.29, issue.6, pp.82-97, 2012.
DOI : 10.1109/MSP.2012.2205597

S. Gould, Darwin: A framework for machine learning and computer vision research and development, Journal of Machine Learning Research, vol.13, pp.3533-3537, 2012.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre et al., Mastering the game of Go with deep neural networks and tree search, Nature, vol.34, issue.7587, pp.484-489, 2016.
DOI : 10.3233/ICG-2011-34302

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature, vol.9, issue.10, pp.115-118, 2017.
DOI : 10.1109/TKDE.2009.191

A. Mahendran and A. Vedaldi, Visualizing Deep Convolutional Neural Networks Using Natural Pre-images, International Journal of Computer Vision, vol.27, issue.2, pp.233-255, 2016.
DOI : 10.1145/279232.279236
URL : http://arxiv.org/pdf/1512.02017

F. Weninger, J. Bergmann, and B. Schuller, Introducing currennt: The munich opensource cuda recurrent neural network toolkit, The Journal of Machine Learning Research, vol.16, issue.1, pp.547-551, 2015.

G. B. Huang, Q. Y. Zhu, and C. K. Siew, Extreme learning machine: Theory and applications, Neurocomputing, vol.70, issue.1-3, pp.489-501, 2006.
DOI : 10.1016/j.neucom.2005.12.126
URL : http://www.ntu.edu.sg/home/egbhuang/ELM-NC-2006.pdf

Y. Y. Lin, J. Y. Chang, and C. T. Lin, Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network, IEEE Transactions on Neural Networks and Learning Systems, vol.24, issue.2, pp.310-321, 2013.
DOI : 10.1109/TNNLS.2012.2231436

F. Yu and X. Xu, A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network, Applied Energy, vol.134, pp.102-113, 2014.
DOI : 10.1016/j.apenergy.2014.07.104

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, vol.85, issue.6088, p.533, 1986.
DOI : 10.1038/323533a0

G. E. Dahl, T. N. Sainath, and G. E. Hinton, Improving deep neural networks for LVCSR using rectified linear units and dropout, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.8609-8613, 2013.
DOI : 10.1109/ICASSP.2013.6639346
URL : http://www.cs.toronto.edu/~gdahl/papers/reluDropoutBN_icassp2013.pdf

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, Journal of machine learning research, vol.15, issue.1, pp.1929-1958, 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

I. Sutskever, O. Vinyals, and Q. V. Le, Sequence to sequence learning with neural networks In: Advances in neural information processing systems, pp.3104-3112, 2014.