From neural PCA to deep unsupervised learning

[1]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[2]  Yoshua Bengio,et al.  How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation , 2014, ArXiv.

[3]  Max Welling,et al.  Semi-supervised Learning with Deep Generative Models , 2014, NIPS.

[4]  Tapani Raiko,et al.  Iterative Neural Autoregressive Distribution Estimator NADE-k , 2014, NIPS.

[5]  Daan Wierstra,et al.  Deep AutoRegressive Networks , 2013, ICML.

[6]  Hugo Larochelle,et al.  A Deep and Tractable Density Estimator , 2013, ICML.

[7]  Yoshua Bengio,et al.  Deep Generative Stochastic Networks Trainable by Backprop , 2013, ICML.

[8]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[9]  Pascal Vincent,et al.  Disentangling Factors of Variation for Facial Expression Recognition , 2012, ECCV.

[10]  Luca Maria Gambardella,et al.  Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.

[11]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[12]  H. Valpola,et al.  Yli-Krekola A bio-inspired computational model of covert attention and learning , 2007 .

[13]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[14]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[15]  Harri Valpola,et al.  Separation of Nonlinear Image Mixtures by Denoising Source Separation , 2006, ICA.

[16]  Harri Valpola,et al.  Denoising Source Separation , 2005, J. Mach. Learn. Res..

[17]  Alexander Ilin,et al.  On the Effect of the Form of the Posterior Approximation in Variational Learning of ICA Models , 2005, Neural Processing Letters.

[18]  E. Rolls,et al.  A Neurodynamical cortical model of visual attention and invariant object recognition , 2004, Vision Research.

[19]  Juha Karhunen,et al.  Hierarchical models of variance sources , 2004, Signal Process..

[20]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[21]  Aapo Hyvärinen,et al.  Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.

[22]  H. Lappalainen.  Fast Algorithms for Bayesian Independent Component Analysis , 2000 .

[23]  M. Veloso,et al.  Latent Variable Models , 2019, Statistical and Econometric Methods for Transportation Data Analysis.

[24]  Aapo Hyvärinen,et al.  Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood , 1998, Neurocomputing.

[25]  Christopher M. Bishop.  Latent Variable Models , 1998, Learning in Graphical Models.

[26]  Erkki Oja,et al.  The nonlinear PCA learning rule in independent component analysis , 1997, Neurocomputing.

[27]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[28]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[29]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[30]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.