Learning multiple layers of representation
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[1] Seungjin Choi,et al. Independent Component Analysis , 2009, Handbook of Natural Computing.
[2] Geoffrey E. Hinton,et al. Modeling image patches with a directed hierarchy of Markov random fields , 2007, NIPS.
[3] Geoffrey E. Hinton,et al. Three new graphical models for statistical language modelling , 2007, ICML '07.
[4] Geoffrey E. Hinton,et al. Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.
[5] Andrew McCallum,et al. Piecewise pseudolikelihood for efficient training of conditional random fields , 2007, ICML '07.
[6] Geoffrey E. Hinton,et al. Unsupervised Learning of Image Transformations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[7] Geoffrey E. Hinton,et al. Learning Multilevel Distributed Representations for High-Dimensional Sequences , 2007, AISTATS.
[8] Marc'Aurelio Ranzato,et al. A Unified Energy-Based Framework for Unsupervised Learning , 2007, AISTATS.
[9] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[10] B. Schölkopf,et al. Modeling Human Motion Using Binary Latent Variables , 2007 .
[11] Yoshua Bengio,et al. Scaling learning algorithms towards AI , 2007 .
[12] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[13] Geoffrey E. Hinton,et al. Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.
[14] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[15] Terrence J. Sejnowski,et al. Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics , 2006, Neural Computation.
[16] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[17] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[18] Nebojsa Jojic,et al. LOCUS: learning object classes with unsupervised segmentation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[19] Michael J. Black,et al. Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[20] Geoffrey E. Hinton,et al. Learning Causally Linked Markov Random Fields , 2005, AISTATS.
[21] Geoffrey E. Hinton,et al. Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.
[22] Bernhard Schölkopf,et al. Training Invariant Support Vector Machines , 2002, Machine Learning.
[23] E. Oja,et al. Independent Component Analysis , 2001 .
[24] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.
[25] Ronald,et al. Learning representations by backpropagating errors , 2004 .
[26] D. Mumford. On the computational architecture of the neocortex , 2004, Biological Cybernetics.
[27] Martin J. Wainwright,et al. Image denoising using scale mixtures of Gaussians in the wavelet domain , 2003, IEEE Trans. Image Process..
[28] M. Lewicki,et al. Learning higher-order structures in natural images , 2003, Network.
[29] Marian Stewart Bartlett,et al. Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.
[30] Geoffrey E. Hinton,et al. A New Learning Algorithm for Mean Field Boltzmann Machines , 2002, ICANN.
[31] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[32] A. Hyvärinen,et al. A multi-layer sparse coding network learns contour coding from natural images , 2002, Vision Research.
[33] Eero P. Simoncelli,et al. Image Denoising using Gaussian Scale Mixtures in the Wavelet Domain , 2002 .
[34] David J. Spiegelhalter,et al. VIBES: A Variational Inference Engine for Bayesian Networks , 2002, NIPS.
[35] Javier R. Movellan,et al. Diffusion Networks, Products of Experts, and Factor Analysis , 2001 .
[36] Peter Dayan,et al. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .
[37] David J. Spiegelhalter,et al. Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.
[38] Zoubin Ghahramani,et al. A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.
[39] Michael I. Jordan. Learning in Graphical Models , 1999, NATO ASI Series.
[40] R. O’Reilly. Six principles for biologically based computational models of cortical cognition , 1998, Trends in Cognitive Sciences.
[41] Emile H. L. Aarts,et al. Boltzmann machines , 1998 .
[42] D. Mumford,et al. The role of the primary visual cortex in higher level vision , 1998, Vision Research.
[43] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[44] Terrence J. Sejnowski,et al. Bayesian Unsupervised Learning of Higher Order Structure , 1996, NIPS.
[45] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[46] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[47] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[48] Frits C. R. Spieksma,et al. Boltzmann Machines , 1995, Artificial Neural Networks.
[49] 大西 仁,et al. Pearl, J. (1988, second printing 1991). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan-Kaufmann. , 1994 .
[50] Geoffrey E. Hinton,et al. Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.
[51] Radford M. Neal. A new view of the EM algorithm that justifies incremental and other variants , 1993 .
[52] D Mumford,et al. On the computational architecture of the neocortex. II. The role of cortico-cortical loops. , 1992, Biological cybernetics.
[53] P. Goldman-Rakic,et al. Preface: Cerebral Cortex Has Come of Age , 1991 .
[54] D. J. Felleman,et al. Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.
[55] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[56] Geoffrey E. Hinton,et al. Learning representations by back-propagation errors, nature , 1986 .