Learning Distributed Representations for Statistical Language Modelling and Collaborative Filtering
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[1] Richard S. Zemel,et al. Collaborative Filtering and the Missing at Random Assumption , 2007, UAI.
[2] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[3] Yihong Gong,et al. Stochastic Relational Models for Large-scale Dyadic Data using MCMC , 2008, NIPS.
[4] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[5] Benjamin M. Marlin,et al. Modeling User Rating Profiles For Collaborative Filtering , 2003, NIPS.
[6] Yoshua Bengio,et al. Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model , 2008, IEEE Transactions on Neural Networks.
[7] John Langford,et al. Conditional Probability Tree Estimation Analysis and Algorithms , 2009, UAI.
[8] Naftali Tishby,et al. Distributional Clustering of English Words , 1993, ACL.
[9] Gediminas Adomavicius,et al. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.
[10] Blockin Blockin,et al. Quick Training of Probabilistic Neural Nets by Importance Sampling , 2003 .
[11] Radford M. Neal,et al. Improving Classification When a Class Hierarchy is Available Using a Hierarchy-Based Prior , 2005, math/0510449.
[12] Yehuda Koren,et al. Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.
[13] Yoshua Bengio,et al. Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.
[14] Joshua Goodman,et al. A bit of progress in language modeling , 2001, Comput. Speech Lang..
[15] Thomas Hofmann,et al. Learning What People (Don't) Want , 2001, ECML.
[16] Nathan Srebro,et al. Fast maximum margin matrix factorization for collaborative prediction , 2005, ICML.
[17] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[18] John F. Canny,et al. Collaborative filtering with privacy via factor analysis , 2002, SIGIR '02.
[19] Jean-Luc Gauvain,et al. Connectionist language modeling for large vocabulary continuous speech recognition , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[20] Geoffrey E. Hinton,et al. Learning Multilevel Distributed Representations for High-Dimensional Sequences , 2007, AISTATS.
[21] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[22] Ruslan Salakhutdinov,et al. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.
[23] Geoffrey E. Hinton,et al. Distributed Representations , 1986, The Philosophy of Artificial Intelligence.
[24] Joshua Goodman,et al. Classes for fast maximum entropy training , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[25] Benjamin M. Marlin,et al. Collaborative Filtering: A Machine Learning Perspective , 2004 .
[26] Domonkos Tikk,et al. Matrix factorization and neighbor based algorithms for the netflix prize problem , 2008, RecSys '08.
[27] Yoshua Bengio,et al. A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..
[28] F ChenStanley,et al. An Empirical Study of Smoothing Techniques for Language Modeling , 1996, ACL.
[29] Richard S. Zemel,et al. The multiple multiplicative factor model for collaborative filtering , 2004, ICML.
[30] David Heckerman,et al. Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.
[31] Geoffrey E. Hinton,et al. Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.
[32] Geoffrey E. Hinton,et al. Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.
[33] Jason Weston,et al. Large-scale kernel machines , 2007 .
[34] Andreas Stolcke,et al. SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.
[35] Geoffrey E. Hinton,et al. Learning distributed representations of concepts. , 1989 .
[36] Thomas G. Dietterich,et al. Editors. Advances in Neural Information Processing Systems , 2002 .
[37] Geoffrey E. Hinton,et al. A Scalable Hierarchical Distributed Language Model , 2008, NIPS.
[38] Robert L. Mercer,et al. Class-Based n-gram Models of Natural Language , 1992, CL.
[39] Radford M. Neal. Probabilistic Inference Using Markov Chain Monte Carlo Methods , 2011 .
[40] Ahmad Emami,et al. Using a connectionist model in a syntactical based language model , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[41] Francis Jack Smith,et al. Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies , 1995, Comput. Linguistics.
[42] John Blitzer,et al. Distributed Latent Variable Models of Lexical Co-occurrences , 2005, AISTATS.
[43] Thomas Hofmann,et al. Probabilistic Latent Semantic Analysis , 1999, UAI.
[44] Yoshua Bengio,et al. Scaling learning algorithms towards AI , 2007 .
[45] Yew Jin Lim. Variational Bayesian Approach to Movie Rating Prediction , 2007 .
[46] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[47] James Bennett,et al. The Netflix Prize , 2007 .
[48] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[49] Fernando Pereira,et al. Aggregate and mixed-order Markov models for statistical language processing , 1997, EMNLP.
[50] M. F.,et al. Bibliography , 1985, Experimental Gerontology.
[51] Holger Schwenk,et al. Continuous space language models , 2007, Comput. Speech Lang..
[52] Tommi S. Jaakkola,et al. Weighted Low-Rank Approximations , 2003, ICML.
[53] Svetha Venkatesh,et al. Ordinal Boltzmann Machines for Collaborative Filtering , 2009, UAI.
[54] Ruslan Salakhutdinov,et al. Probabilistic Matrix Factorization , 2007, NIPS.
[55] Marc'Aurelio Ranzato,et al. Semi-supervised learning of compact document representations with deep networks , 2008, ICML '08.
[56] Miguel Á. Carreira-Perpiñán,et al. On Contrastive Divergence Learning , 2005, AISTATS.
[57] Geoffrey E. Hinton,et al. Three new graphical models for statistical language modelling , 2007, ICML '07.
[58] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[59] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.
[60] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[61] Arkadiusz Paterek,et al. Improving regularized singular value decomposition for collaborative filtering , 2007 .
[62] Philipp Slusallek,et al. Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.
[63] Tommi S. Jaakkola,et al. Maximum-Margin Matrix Factorization , 2004, NIPS.
[64] Joseph Hilbe,et al. Data Analysis Using Regression and Multilevel/Hierarchical Models , 2009 .
[65] John Blitzer,et al. Hierarchical Distributed Representations for Statistical Language Modeling , 2004, NIPS.