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[1] Nitish Srivastava,et al. Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.
[2] Bruno A. Olshausen,et al. Principles of Image Representation in Visual Cortex , 2003 .
[3] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[4] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[5] Jürgen Schmidhuber,et al. LSTM can Solve Hard Long Time Lag Problems , 1996, NIPS.
[6] Olivier Sigaud,et al. Towards Deep Developmental Learning , 2016, IEEE Transactions on Cognitive and Developmental Systems.
[7] Roland Memisevic,et al. Learning to Relate Images , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Daniel Jiwoong Im. Analyzing the Dynamics of Gated Auto-encoders , 2014 .
[9] Yann LeCun,et al. Convolutional Learning of Spatio-temporal Features , 2010, ECCV.
[10] Roland Memisevic,et al. Modeling Deep Temporal Dependencies with Recurrent "Grammar Cells" , 2014, NIPS.
[11] Manuel Lopes,et al. Learning Object Affordances: From Sensory--Motor Coordination to Imitation , 2008, IEEE Transactions on Robotics.
[12] Olivier Sigaud,et al. Deep unsupervised network for multimodal perception, representation and classification , 2015, Robotics Auton. Syst..
[13] Alain Droniou,et al. Apprentissage de représentations et robotique développementale : quelques apports de l'apprentissage profond pour la robotique autonome. (Representation learning and developmental robotics : on the use of deep learning for autonomous robots) , 2015 .
[14] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[15] Aapo Hyvärinen,et al. Estimation of Non-Normalized Statistical Models by Score Matching , 2005, J. Mach. Learn. Res..
[16] Geoffrey E. Hinton,et al. Unsupervised Learning of Image Transformations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Graham W. Taylor,et al. "Mental Rotation" by Optimizing Transforming Distance , 2014, ArXiv.
[18] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[19] Geoffrey E. Hinton,et al. Gated Softmax Classification , 2010, NIPS.
[20] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[21] Pascal Vincent,et al. A Connection Between Score Matching and Denoising Autoencoders , 2011, Neural Computation.
[22] Olivier Sigaud,et al. Gated Autoencoders with Tied Input Weights , 2013, ICML.
[23] Haitham Bou-Ammar,et al. Factored four way conditional restricted Boltzmann machines for activity recognition , 2015, Pattern Recognit. Lett..
[24] Afshin Dehghan,et al. Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[26] Roland Memisevic,et al. On multi-view feature learning , 2012, ICML.
[27] Oliver Brock,et al. Patterns for Learning with Side Information , 2015, 1511.06429.
[28] Roland Memisevic,et al. Non-linear Latent Factor Models for Revealing Structure in High-dimensional Data , 2008 .
[29] James Martens,et al. Deep learning via Hessian-free optimization , 2010, ICML.
[30] Geoffrey E. Hinton,et al. Two Distributed-State Models For Generating High-Dimensional Time Series , 2011, J. Mach. Learn. Res..
[31] Geoffrey E. Hinton,et al. Generating Text with Recurrent Neural Networks , 2011, ICML.
[32] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[33] Yoshua Bengio,et al. Deep Learning of Representations: Looking Forward , 2013, SLSP.
[34] Roland Memisevic,et al. Learning Visual Odometry with a Convolutional Network , 2015, VISAPP.
[35] Nando de Freitas,et al. A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets , 2010, 2010 Information Theory and Applications Workshop (ITA).
[36] Olivier Sigaud,et al. Learning a repertoire of actions with deep neural networks , 2014, 4th International Conference on Development and Learning and on Epigenetic Robotics.
[37] Roland Memisevic,et al. Modeling sequential data using higher-order relational features and predictive training , 2014, ArXiv.
[38] Geoffrey E. Hinton,et al. Factored conditional restricted Boltzmann Machines for modeling motion style , 2009, ICML '09.
[39] Roland Memisevic,et al. Gradient-based learning of higher-order image features , 2011, 2011 International Conference on Computer Vision.
[40] Geoffrey E. Hinton,et al. Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines , 2010, Neural Computation.
[41] Graham W. Taylor,et al. Generative Class-conditional Autoencoders , 2015, ICLR.
[42] Droniou Alain,et al. Gated Autoencoders with Tied Input Weights , 2013, ICML 2013.
[43] IvaldiSerena,et al. Deep unsupervised network for multimodal perception, representation and classification , 2015 .
[44] Aapo Hyv. Estimation of Non-Normalized Statistical Models by Score Matching , 2005 .
[45] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[46] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[47] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .