Training restricted Boltzmann machines: An introduction
暂无分享,去创建一个
[1] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[2] Razvan Pascanu,et al. Learning Algorithms for the Classification Restricted Boltzmann Machine , 2012, J. Mach. Learn. Res..
[3] Tapani Raiko,et al. Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines , 2011, ICML.
[4] Ruslan Salakhutdinov,et al. On the quantitative analysis of deep belief networks , 2008, ICML '08.
[5] Klaus-Robert Müller,et al. Deep Boltzmann Machines and the Centering Trick , 2012, Neural Networks: Tricks of the Trade.
[6] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[7] Geoffrey E. Hinton,et al. Replicated Softmax: an Undirected Topic Model , 2009, NIPS.
[8] Geoffrey E. Hinton,et al. Phone recognition using Restricted Boltzmann Machines , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[9] Geoffrey E. Hinton,et al. Using fast weights to improve persistent contrastive divergence , 2009, ICML '09.
[10] Yoshua Bengio,et al. Better Mixing via Deep Representations , 2012, ICML.
[11] P. Diaconis,et al. COMPARISON THEOREMS FOR REVERSIBLE MARKOV CHAINS , 1993 .
[12] D. Woodard,et al. Sufficient Conditions for Torpid Mixing of Parallel and Simulated Tempering , 2009 .
[13] Tapani Raiko,et al. Enhanced Gradient for Training Restricted Boltzmann Machines , 2013, Neural Computation.
[14] Madeleine B. Thompson. A Comparison of Methods for Computing Autocorrelation Time , 2010, 1011.0175.
[15] Ilya Sutskever,et al. On the Convergence Properties of Contrastive Divergence , 2010, AISTATS.
[16] Pascal Vincent,et al. Quickly Generating Representative Samples from an RBM-Derived Process , 2011, Neural Computation.
[17] Klaus-Robert Müller,et al. Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.
[18] Geoffrey E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.
[19] Christian Igel,et al. The flip-the-state transition operator for restricted Boltzmann machines , 2013, Machine Learning.
[20] Emile H. L. Aarts,et al. Boltzmann machines , 1998 .
[21] Ron Meir,et al. Density Estimation Through Convex Combinations of Densities: Approximation and Estimation Bounds , 1997, Neural Networks.
[22] Yann LeCun,et al. Convolutional Learning of Spatio-temporal Features , 2010, ECCV.
[23] Madeleine B. Thompson. Introduction to SamplerCompare , 2011 .
[24] John Odentrantz,et al. Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues , 2000, Technometrics.
[25] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[26] C. Geyer. Markov Chain Monte Carlo Maximum Likelihood , 1991 .
[27] Nicolas Le Roux,et al. Representational Power of Restricted Boltzmann Machines and Deep Belief Networks , 2008, Neural Computation.
[28] P. Peskun,et al. Optimum Monte-Carlo sampling using Markov chains , 1973 .
[29] P. Diaconis,et al. LOGARITHMIC SOBOLEV INEQUALITIES FOR FINITE MARKOV CHAINS , 1996 .
[30] Wang,et al. Nonuniversal critical dynamics in Monte Carlo simulations. , 1987, Physical review letters.
[31] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[32] Geoffrey E. Hinton,et al. Factored conditional restricted Boltzmann Machines for modeling motion style , 2009, ICML '09.
[33] Geoffrey E. Hinton,et al. Conditional Restricted Boltzmann Machines for Structured Output Prediction , 2011, UAI.
[34] Michael I. Jordan. Graphical Models , 1998 .
[35] Yoshua Bengio,et al. Unsupervised Models of Images by Spikeand-Slab RBMs , 2011, ICML.
[36] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[37] Radford M. Neal. Estimating Ratios of Normalizing Constants Using Linked Importance Sampling , 2005, math/0511216.
[38] Nan Wang,et al. An analysis of Gaussian-binary restricted Boltzmann machines for natural images , 2012, ESANN.
[39] Honglak Lee,et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.
[40] Nicolas Le Roux,et al. Deep Belief Networks Are Compact Universal Approximators , 2010, Neural Computation.
[41] R. Swendsen,et al. Cluster Monte Carlo algorithms , 1990 .
[42] Tafsir Thiam,et al. The Boltzmann machine , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).
[43] Igel Christian,et al. Contrastive Divergence Learning May Diverge When Training Restricted Boltzmann Machines , 2009 .
[44] G. Crooks. Path-ensemble averages in systems driven far from equilibrium , 1999, cond-mat/9908420.
[45] Yoshua Bengio,et al. Classification using discriminative restricted Boltzmann machines , 2008, ICML '08.
[46] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[47] Shun-ichi Amari,et al. Information geometry of Boltzmann machines , 1992, IEEE Trans. Neural Networks.
[48] Benjamin Schwehn. Using the Natural Gradient for training Restricted Boltzmann Machines , 2010 .
[49] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[50] Geoffrey E. Hinton,et al. Generative versus discriminative training of RBMs for classification of fMRI images , 2008, NIPS.
[51] Christian Igel,et al. A bound for the convergence rate of parallel tempering for sampling restricted Boltzmann machines , 2015, Theor. Comput. Sci..
[52] Tapani Raiko,et al. Gaussian-Bernoulli deep Boltzmann machine , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[53] Yoshua Bengio,et al. On Tracking The Partition Function , 2011, NIPS.
[54] Radford M. Neal. Probabilistic Inference Using Markov Chain Monte Carlo Methods , 2011 .
[55] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[56] D. Woodard,et al. Conditions for Rapid and Torpid Mixing of Parallel and Simulated Tempering on Multimodal Distributions , 2009, 0906.2341.
[57] Yoshua Bengio,et al. Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.
[58] Christian Igel,et al. Empirical evaluation of the improved Rprop learning algorithms , 2003, Neurocomputing.
[59] 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).
[60] Christian Igel,et al. Bounding the Bias of Contrastive Divergence Learning , 2011, Neural Computation.
[61] Xiao-Li Meng,et al. Simulating Normalizing Constants: From Importance Sampling to Bridge Sampling to Path Sampling , 1998 .
[62] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[63] Oswin Krause,et al. Approximation properties of DBNs with binary hidden units and real-valued visible units , 2013, ICML.
[64] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[65] Christian Osendorfer,et al. Music Similarity Estimation with the Mean-Covariance Restricted Boltzmann Machine , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.
[66] Martin A. Riedmiller,et al. Advanced supervised learning in multi-layer perceptrons — From backpropagation to adaptive learning algorithms , 1994 .
[67] Alan L. Yuille,et al. The Convergence of Contrastive Divergences , 2004, NIPS.
[68] Christian Igel,et al. An Introduction to Restricted Boltzmann Machines , 2012, CIARP.
[69] Geoffrey E. Hinton,et al. Robust Boltzmann Machines for recognition and denoising , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[70] Tijmen Tieleman,et al. Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.
[71] Miguel Á. Carreira-Perpiñán,et al. On Contrastive Divergence Learning , 2005, AISTATS.
[72] Pascal Vincent,et al. Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines , 2010, AISTATS.
[73] Persi Diaconis,et al. What Do We Know about the Metropolis Algorithm? , 1998, J. Comput. Syst. Sci..
[74] P. Tavan,et al. Efficiency of exchange schemes in replica exchange , 2009 .
[75] Yoshua Bengio,et al. Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs , 2010, ArXiv.
[76] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[77] S. Adler. Over-relaxation method for the Monte Carlo evaluation of the partition function for multiquadratic actions , 1981 .
[78] Razvan Pascanu,et al. Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines , 2013, ICLR.
[79] Christopher K. I. Williams,et al. Multiple Texture Boltzmann Machines , 2012, AISTATS.
[80] Pascal Vincent,et al. Parallel Tempering for Training of Restricted Boltzmann Machines , 2010 .
[81] Tapani Raiko,et al. Parallel tempering is efficient for learning restricted Boltzmann machines , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).
[82] Andreas C. Müller,et al. Investigating Convergence of Restricted Boltzmann Machine Learning , 2010 .
[83] Jun S. Liu,et al. Metropolized independent sampling with comparisons to rejection sampling and importance sampling , 1996, Stat. Comput..
[84] Nicolas Le Roux,et al. Learning a Generative Model of Images by Factoring Appearance and Shape , 2011, Neural Computation.
[85] Geoffrey E. Hinton,et al. Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.
[86] Michael R. Shirts,et al. Equilibrium free energies from nonequilibrium measurements using maximum-likelihood methods. , 2003, Physical review letters.
[87] Geoffrey E. Hinton,et al. Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.
[88] Rong Yan,et al. Mining Associated Text and Images with Dual-Wing Harmoniums , 2005, UAI.
[89] Neal Madras,et al. On the swapping algorithm , 2003, Random Struct. Algorithms.
[90] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[91] Geoffrey E. Hinton. Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.
[92] Kazuyuki Aihara,et al. Robust Generation of Dynamical Patterns in Human Motion by a Deep Belief Nets , 2011, ACML.
[93] Nihat Ay,et al. Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines , 2010, Neural Computation.
[94] Yoshua Bengio,et al. An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.
[95] Andrew R. Barron,et al. Mixture Density Estimation , 1999, NIPS.
[96] Geoffrey E. Hinton,et al. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.
[97] Geoffrey E. Hinton,et al. Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.
[98] Wolff,et al. Collective Monte Carlo updating for spin systems. , 1989, Physical review letters.
[99] Yoshua Bengio,et al. Justifying and Generalizing Contrastive Divergence , 2009, Neural Computation.
[100] Charles H. Bennett,et al. Efficient estimation of free energy differences from Monte Carlo data , 1976 .
[101] Ilya Sutskever,et al. Data Normalization in the Learning of Restricted Boltzmann Machines , 2011 .
[102] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[103] Max Welling,et al. Product of experts , 2007, Scholarpedia.
[104] Dana Randall,et al. Torpid mixing of simulated tempering on the Potts model , 2004, SODA '04.
[105] Radford M. Neal. Annealed importance sampling , 1998, Stat. Comput..
[106] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[107] Benjamin Schrauwen,et al. Training Restricted Boltzmann Machines with Multi-tempering: Harnessing Parallelization , 2012, ICANN.
[108] Tapani Raiko,et al. Deep Learning Made Easier by Linear Transformations in Perceptrons , 2012, AISTATS.
[109] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[110] Christian Igel,et al. Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines , 2010, ICANN.
[111] R. Salakhutdinov. Learning and Evaluating Boltzmann Machines , 2008 .
[112] Honglak Lee,et al. Unsupervised feature learning for audio classification using convolutional deep belief networks , 2009, NIPS.
[113] Wang,et al. Replica Monte Carlo simulation of spin glasses. , 1986, Physical review letters.
[114] Christian Igel,et al. Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives , 2011, ESANN.
[115] D. Randall,et al. Markov chain decomposition for convergence rate analysis , 2002 .
[116] Peter V. Gehler,et al. The rate adapting poisson model for information retrieval and object recognition , 2006, ICML.
[117] D. Mackay,et al. Failures of the One-Step Learning Algorithm , 2001 .
[118] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[119] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[120] 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.
[121] Ruslan Salakhutdinov,et al. Learning in Markov Random Fields using Tempered Transitions , 2009, NIPS.
[122] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[123] Nan Wang,et al. How to Center Binary Restricted Boltzmann Machines , 2013, ArXiv.
[124] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .