An Efficient Learning Procedure for Deep

[1]  Ruslan Salakhutdinov,et al.  Learning Deep Boltzmann Machines using Adaptive MCMC , 2010, ICML.

[2]  Pascal Vincent,et al.  Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines , 2010, AISTATS.

[3]  Hugo Larochelle,et al.  Efficient Learning of Deep Boltzmann Machines , 2010, AISTATS.

[4]  Geoffrey E. Hinton,et al.  Replicated Softmax: an Undirected Topic Model , 2009, NIPS.

[5]  Max Welling,et al.  Herding dynamical weights to learn , 2009, ICML '09.

[6]  Geoffrey E. Hinton,et al.  Using fast weights to improve persistent contrastive divergence , 2009, ICML '09.

[7]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[8]  Ruslan Salakhutdinov,et al.  Evaluating probabilities under high-dimensional latent variable models , 2008, NIPS.

[9]  Geoffrey E. Hinton,et al.  Implicit Mixtures of Restricted Boltzmann Machines , 2008, NIPS.

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

[11]  Ruslan Salakhutdinov,et al.  On the quantitative analysis of deep belief networks , 2008, ICML '08.

[12]  Tijmen Tieleman,et al.  Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.

[13]  Geoffrey E. Hinton,et al.  Modeling image patches with a directed hierarchy of Markov random fields , 2007, NIPS.

[14]  Jason Weston,et al.  Large-scale kernel machines , 2007 .

[15]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[16]  Marc'Aurelio Ranzato,et al.  Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

[18]  Geoffrey E. Hinton,et al.  Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.

[19]  H. Robbins A Stochastic Approximation Method , 1951 .

[20]  Yoshua Bengio,et al.  Scaling learning algorithms towards AI , 2007 .

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

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

[23]  Miguel Á. Carreira-Perpiñán,et al.  On Contrastive Divergence Learning , 2005, AISTATS.

[24]  Geoffrey E. Hinton,et al.  Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.

[25]  Alan L. Yuille,et al.  The Convergence of Contrastive Divergences , 2004, NIPS.

[26]  Geoffrey E. Hinton,et al.  Neighbourhood Components Analysis , 2004, NIPS.

[27]  Y. LeCun,et al.  Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[28]  Bernhard Schölkopf,et al.  Training Invariant Support Vector Machines , 2002, Machine Learning.

[29]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[30]  Geoffrey E. Hinton Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[31]  Christopher K. I. Williams,et al.  An analysis of contrastive divergence learning in gaussian boltzmann machines , 2002 .

[32]  Radford M. Neal Annealed importance sampling , 1998, Stat. Comput..

[33]  Javier R. Movellan,et al.  Diffusion Networks, Products of Experts, and Factor Analysis , 2001 .

[34]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[35]  Hilbert J. Kappen,et al.  Boltzmann Machine Learning Using Mean Field Theory and Linear Response Correction , 1997, NIPS.

[36]  R. Zemel A minimum description length framework for unsupervised learning , 1994 .

[37]  Geoffrey E. Hinton,et al.  Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.

[38]  Radford M. Neal Connectionist Learning of Belief Networks , 1992, Artif. Intell..

[39]  Conrad Galland,et al.  Learning in Deterministic Boltzmann Machine Networks , 1992 .

[40]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[41]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[43]  Geoffrey E. Hinton,et al.  OPTIMAL PERCEPTUAL INFERENCE , 1983 .