Y. Bengio, Deep Learning of Representations: Looking Forward, Statistical language and speech processing, pp.1-37, 2013.
DOI : 10.1007/978-3-642-39593-2_1

A. Dehghan, E. G. Ortiz, R. Villegas, and M. Shah, Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1757-1764, 2014.
DOI : 10.1109/CVPR.2014.227

W. Ding and G. W. Taylor, Mental rotation " by optimizing transforming distance. arXiv preprint, 2014.

A. Droniou, Apprentissage de représentations et robotique développementale: quelques apports de l'apprentissage profond pour la robotique autonome

A. Droniou and O. Sigaud, Gated autoencoders with tied input weights, Proceedings of the 29th International Conference on Machine Learning, pp.1-6, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00817035

A. Droniou, . Ivaldi, . Serena, and O. Sigaud, Learning a repertoire of actions with deep neural networks, 4th International Conference on Development and Learning and on Epigenetic Robotics, pp.1-6, 2014.
DOI : 10.1109/DEVLRN.2014.6982986

URL : https://hal.archives-ouvertes.fr/hal-01065741

A. Droniou, . Ivaldi, . Serena, and O. Sigaud, Deep unsupervised network for multimodal perception, representation and classification, Robotics and Autonomous Systems, vol.71, pp.83-98, 2015.
DOI : 10.1016/j.robot.2014.11.005

URL : https://hal.archives-ouvertes.fr/hal-01083521

X. Glorot, . Bordes, . Antoine, and Y. Bengio, Deep sparse rectifier neural networks, International Conference on Artificial Intelligence and Statistics, pp.315-323, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00752497

G. E. Hinton and R. R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.313504-507, 2006.
DOI : 10.1126/science.1127647

S. Hochreiter and J. Schmidhuber, LSTM can solve hard long time lag problems, Advances in Neural Information Processing Systems 9: Proceedings of the 1996 Conference, p.473, 1997.

S. Hochreiter and J. Schmidhuber, LSTM can solve hard long time lag problems, Advances in Neural Information Processing Systems 9: Proceedings of the 1996 Conference, p.473, 1997.

A. Hyvärinen, Estimation of non-normalized statistical models by score matching, Journal of Machine Learning Research, pp.695-709, 2005.

D. Im, . Jiwoong, and G. W. Taylor, Analyzing the dynamics of gated auto-encoders in practice. Unpublished manuscript?, 2014.

D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques, 2009.

K. Konda and R. Memisevic, Learning Visual Odometry with a Convolutional Network, Proceedings of the 10th International Conference on Computer Vision Theory and Applications, 2015.
DOI : 10.5220/0005299304860490

Y. Lecun, . Bottou, . Léon, . Bengio, . Yoshua et al., Gradient-based learning applied to document recognition, Proceedings of the IEEE, pp.2278-2324, 1998.
DOI : 10.1109/5.726791

Y. Lecun, J. Bengio, and G. E. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015.
DOI : 10.1007/s10994-013-5335-x

T. P. Lillicrap, J. J. Hunt, . Pritzel, . Alexander, . Heess et al., Continuous control with deep reinforcement learning. arXiv preprint, 2015.

J. Martens, Deep learning via hessian-free optimization, Proceedings of the 27th International Conference on Machine Learning, pp.735-742, 2010.

R. Memisevic, Non-linear latent factor models for revealing structure in high-dimensional data, 2008.

R. Memisevic, Gradient-based learning of higher-order image features, 2011 International Conference on Computer Vision, pp.1591-1598, 2011.
DOI : 10.1109/ICCV.2011.6126419

R. Memisevic, On multi-view feature learning, Proceedings of the 28th Annual International Conference on Machine Learning, pp.1-8, 2012.

R. Memisevic, Learning to Relate Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1829-1846, 2013.
DOI : 10.1109/TPAMI.2013.53

R. Memisevic and G. E. Hinton, Unsupervised Learning of Image Transformations, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383036

R. Memisevic and G. E. Hinton, Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines, Neural Computation, vol.17, issue.6, pp.1473-1492, 2010.
DOI : 10.1007/3-540-47969-4_30

R. Memisevic, . Zach, . Christopher, G. E. Hinton, and M. Pollefeys, Gated softmax classification, Advances in Neural Information Processing Systems, pp.1603-1611, 2010.

. Michalski, . Vincent, R. Memisevic, and K. Konda, Modeling sequential data using higher-order relational features and predictive training, 2014.

. Michalski, . Vincent, R. Memisevic, and K. Konda, Modeling deep temporal dependencies with recurrent " grammar cells, Advances in neural information processing systems, pp.1925-1933, 2014.

. Mnih, . Volodymyr, . Kavukcuoglu, . Koray, . Silver et al., Human-level control through deep reinforcement learning, Nature, vol.101, issue.7540, pp.518529-533, 2015.
DOI : 10.1038/nature14236

D. Mocanu, . Constantin, H. Ammar, . Bou, . Lowet et al., Factored four way conditional restricted Boltzmann machines for activity recognition, Pattern Recognition Letters, vol.66, 2015.
DOI : 10.1016/j.patrec.2015.01.013

L. Montesano, M. Lopes, A. Bernardino, S. , and J. , Learning Object Affordances: From Sensory--Motor Coordination to Imitation, IEEE Transactions on Robotics, vol.24, issue.1, pp.15-26, 2008.
DOI : 10.1109/TRO.2007.914848

B. A. Olshausen, Principles of image representation in visual cortex. The visual neurosciences, pp.1603-1615, 2003.

J. Rudy and G. W. Taylor, Generative class-conditional autoencoders. arXiv preprint, 2014.

O. Sigaud and A. Droniou, Towards Deep Developmental Learning, IEEE Transactions on Cognitive and Developmental Systems, vol.8, issue.2, 2016.
DOI : 10.1109/TAMD.2015.2496248

URL : https://hal.archives-ouvertes.fr/hal-01331799

P. Smolensky, Information processing in dynamical systems: foundations of harmony theory, Parallel distributed processing: explorations in the microstructure of cognition, pp.194-281, 1986.

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

N. Srivastava, . Mansimov, . Elman, and R. Salakhutdinov, Unsupervised learning of video representations using LSTMs, 2015.

. Sutskever, . Ilya, J. Martens, and G. E. Hinton, Generating text with recurrent neural networks, Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp.1017-1024, 2011.

K. Swersky, . Chen, . Bo, . Marlin, D. Benjamin et al., A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets, 2010 Information Theory and Applications Workshop (ITA), pp.1-10, 2010.
DOI : 10.1109/ITA.2010.5454138

G. W. Taylor and G. E. Hinton, Factored conditional restricted Boltzmann Machines for modeling motion style, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.1025-1032, 2009.
DOI : 10.1145/1553374.1553505

G. W. Taylor, . Fergus, . Rob, . Lecun, . Yann et al., Convolutional Learning of Spatio-temporal Features, ECCV'10, pp.140-153, 2010.
DOI : 10.1007/978-3-642-15567-3_11

G. W. Taylor, G. E. Hinton, R. Sam, and T. , Two Distributed-State Models For Generating High-Dimensional Time Series, The Journal of Machine Learning Research, vol.12, pp.1025-1068

P. Vincent, A Connection Between Score Matching and Denoising Autoencoders, Neural Computation, vol.11, issue.7, pp.1661-1674, 2011.
DOI : 10.1007/3-540-46084-5_57

P. Vincent, . Larochelle, . Hugo, . Bengio, . Yoshua et al., Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.1096-1103, 2008.
DOI : 10.1145/1390156.1390294