Learning Algorithms for the Classification Restricted Boltzmann Machine
暂无分享,去创建一个
Razvan Pascanu | Yoshua Bengio | Michael I. Mandel | Hugo Larochelle | Yoshua Bengio | H. Larochelle | Razvan Pascanu
[1] J. Besag. Statistical Analysis of Non-Lattice Data , 1975 .
[2] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[3] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[4] Michael I. Jordan,et al. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.
[5] M. Opper,et al. Comparing the Mean Field Method and Belief Propagation for Approximate Inference in MRFs , 2001 .
[6] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[7] Geoffrey E. Hinton,et al. A New Learning Algorithm for Mean Field Boltzmann Machines , 2002, ICANN.
[8] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[9] Mehryar Mohri,et al. AUC Optimization vs. Error Rate Minimization , 2003, NIPS.
[10] Geoffrey E. Hinton,et al. Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.
[11] Guillaume Bouchard,et al. The Tradeoff Between Generative and Discriminative Classifiers , 2004 .
[12] Rong Yan,et al. Mining Associated Text and Images with Dual-Wing Harmoniums , 2005, UAI.
[13] Nicolas Le Roux,et al. The Curse of Highly Variable Functions for Local Kernel Machines , 2005, NIPS.
[14] M. Pretti. A message-passing algorithm with damping , 2005 .
[15] Max Welling,et al. Learning in Markov Random Fields with Contrastive Free Energies , 2005, AISTATS.
[16] Peter V. Gehler,et al. The rate adapting poisson model for information retrieval and object recognition , 2006, ICML.
[17] Christopher Joseph Pal,et al. Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification , 2006, AAAI.
[18] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[19] Aapo Hyvärinen,et al. Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines , 2006, Neural Computation.
[20] Alexander Zien,et al. Semi-Supervised Learning , 2006 .
[21] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[22] Bernhard Schölkopf,et al. Introduction to Semi-Supervised Learning , 2006, Semi-Supervised Learning.
[23] Alexander Zien,et al. Label Propagation and Quadratic Criterion , 2006 .
[24] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[25] Eric P. Xing,et al. Harmonium Models for Semantic Video Representation and Classification , 2007, SDM.
[26] Geoffrey E. Hinton,et al. To recognize shapes, first learn to generate images. , 2007, Progress in brain research.
[27] John F. Kalaska,et al. Computational neuroscience : theoretical insights into brain function , 2007 .
[28] Christopher Joseph Pal,et al. Semi-supervised classification with hybrid generative/discriminative methods , 2007, KDD '07.
[29] Yoshua Bengio,et al. An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.
[30] Geoffrey E. Hinton,et al. Three new graphical models for statistical language modelling , 2007, ICML '07.
[31] Daniel P. W. Ellis,et al. Please Scroll down for Article Journal of New Music Research a Web-based Game for Collecting Music Metadata a Web-based Game for Collecting Music Metadata , 2022 .
[32] Geoffrey E. Hinton,et al. Generative versus discriminative training of RBMs for classification of fMRI images , 2008, NIPS.
[33] Tijmen Tieleman,et al. Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.
[34] Michael I. Jordan,et al. An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators , 2008, ICML '08.
[35] Yoshua Bengio,et al. Classification using discriminative restricted Boltzmann machines , 2008, ICML '08.
[36] Paul Lamere,et al. Social Tagging and Music Information Retrieval , 2008 .
[37] Geoffrey E. Hinton,et al. Using fast weights to improve persistent contrastive divergence , 2009, ICML '09.
[38] Geoffrey E. Hinton,et al. Semantic hashing , 2009, Int. J. Approx. Reason..
[39] Rossano Schifanella,et al. Folks in Folksonomies: social link prediction from shared metadata , 2010, WSDM '10.
[40] Yoshua Bengio,et al. Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.
[41] Douglas Eck,et al. Learning Tags that Vary Within a Song , 2010, ISMIR.
[42] Geoffrey E. Hinton,et al. Gated Softmax Classification , 2010, NIPS.
[43] Nicolas Le Roux,et al. Deep Belief Networks Are Compact Universal Approximators , 2010, Neural Computation.
[44] Andrew Gelfand,et al. On Herding and the Perceptron Cycling Theorem , 2010, NIPS.
[45] Padhraic Smyth,et al. Learning with Blocks: Composite Likelihood and Contrastive Divergence , 2010, AISTATS.
[46] Max Welling,et al. Hidden-Unit Conditional Random Fields , 2011, AISTATS.
[47] Razvan Pascanu,et al. Contextual tag inference , 2011, TOMCCAP.
[48] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[49] Razvan Pascanu,et al. Autotagging music with conditional restricted Boltzmann machines , 2011, ArXiv.
[50] Geoffrey E. Hinton,et al. Conditional Restricted Boltzmann Machines for Structured Output Prediction , 2011, UAI.
[51] Hugo Larochelle,et al. Classification of Sets using Restricted Boltzmann Machines , 2011, UAI.
[52] Harry Joe,et al. Composite Likelihood Methods , 2012 .