A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.
DOI : 10.1145/3065386
URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

D. Ciregan, U. Meier, and J. Schmidhuber, Multi-column deep neural networks for image classification, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp.3642-3649, 2012.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed et al., Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, Signal Processing Magazine, IEEE, vol.29, issue.6, pp.82-97, 2012.

E. George, D. Dahl, L. Yu, A. Deng, and . Acero, Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, IEEE Transactions on Audio, Speech, and Language Processing, vol.20, issue.1, pp.30-42, 2012.

A. Graves, M. Abdel-rahman, and G. Hinton, Speech recognition with deep recurrent neural networks, Acoustics, speech and signal processing (icassp), 2013 ieee international conference on, pp.6645-6649, 2013.
DOI : 10.1109/icassp.2013.6638947

T. Mikolov, M. Karafiát, L. Burget, J. Cernock?, and S. Khudanpur, Recurrent neural network based language model, Interspeech, vol.2, p.3, 2010.

Y. Ken-ichi-funahashi and . Nakamura, Approximation of dynamical systems by continuous time recurrent neural networks, Neural networks, vol.6, issue.6, pp.801-806, 1993.

J. Felix-a-gers, F. Schmidhuber, and . Cummins, Learning to forget: Continual prediction with lstm, 1999.

A. Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional lstm and other neural network architectures, Neural Networks, vol.18, issue.5, pp.602-610, 2005.
DOI : 10.1016/j.neunet.2005.06.042

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.

P. Arena, L. Fortuna, G. Muscato, and M. G. Xibilia, Multilayer perceptrons to approximate quaternion valued functions, Neural Networks, vol.10, issue.2, pp.335-342, 1997.
DOI : 10.1016/s0893-6080(96)00048-2

T. Parcollet, M. Morchid, P. Bousquet, R. Dufour, G. Linarès et al., Quaternion neural networks for spoken language understanding, Spoken Language Technology Workshop (SLT), pp.362-368, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02107532

M. Morchid, G. Linarès, M. El-beze, and R. D. Mori, Theme identification in telephone service conversations using quaternions of speech features, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01339930

T. Isokawa, T. Kusakabe, N. Matsui, and F. Peper, Quaternion neural network and its application," in Knowledge-based intelligent information and engineering systems, pp.318-324, 2003.

H. William-rowan, Elements of quaternions, p.1866

R. Collobert and J. Weston, A unified architecture for natural language processing: Deep neural networks with multitask learning, Proceedings of the 25th international conference on Machine learning, pp.160-167, 2008.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, International conference on artificial intelligence and statistics, pp.249-256, 2010.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

Y. Bengio, Learning deep architectures for ai, Foundations and trends R in Machine Learning, vol.2, pp.1-127, 2009.

R. Salakhutdinov and G. Hinton, Deep boltzmann machines, Artificial Intelligence and Statistics, pp.448-455, 2009.

Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, Greedy layer-wise training of deep networks, Advances in neural information processing systems, vol.19, p.153, 2007.

T. Minemoto, T. Isokawa, H. Nishimura, and N. Matsui, Feed forward neural network with random quaternionic neurons, Signal Processing, vol.136, pp.59-68, 2017.
DOI : 10.1016/j.sigpro.2016.11.008

M. David, . Blei, Y. Andrew, and M. Ng, Latent dirichlet allocation, the Journal of machine Learning research, vol.3, pp.993-1022, 2003.

I. L. Kantor, A. S. Solodovnikov, and A. Shenitzer, Hypercomplex numbers: an elementary introduction to algebras, 1989.

B. Jack and . Kuipers, Quaternions and rotation sequences, 1999.

F. Zhang, Quaternions and matrices of quaternions, Linear algebra and its applications, vol.251, pp.21-57, 1997.

J. P. Ward, Quaternions and Cayley numbers: Algebra and applications, vol.403, 1997.
DOI : 10.1007/978-94-011-5768-1

D. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

D. Matthew and . Zeiler, Adadelta: an adaptive learning rate method, 2012.

D. Erhan, Y. Bengio, A. Courville, P. Manzagol, P. Vincent et al., Why does unsupervised pre-training help deep learning?, Journal of Machine Learning Research, vol.11, pp.625-660, 2010.

J. John, . Godfrey, C. Edward, J. Holliman, and . Mcdaniel, Switchboard: Telephone speech corpus for research and development, Acoustics, Speech, and Signal Processing, vol.1, pp.517-520, 1992.

F. Bechet, B. Maza, N. Bigouroux, T. Bazillon, M. El-beze et al., Decoda: a call-centre human-human spoken conversation corpus, LREC, pp.1343-1347, 2012.

G. Linares, P. Nocéra, D. Massonie, and D. Matrouf, The lia speech recognition system: from 10xrt to 1xrt, Text, Speech and Dialogue, pp.302-308, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01318314

K. Janod, M. Morchid, R. Dufour, G. Linares, and R. D. Mori, Deep stacked autoencoders for spoken language understanding, ISCA INTERSPEECH, vol.1, issue.2, 2016.
DOI : 10.21437/interspeech.2016-63