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JMLR
2012
12 years 16 days ago
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models
The development of accurate models and efficient algorithms for the analysis of multivariate categorical data are important and longstanding problems in machine learning and compu...
Mohammad Emtiyaz Khan, Shakir Mohamed, Benjamin M....
ICML
2005
IEEE
14 years 11 months ago
Learning as search optimization: approximate large margin methods for structured prediction
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...
Daniel Marcu, Hal Daumé III
NECO
2008
170views more  NECO 2008»
13 years 10 months ago
Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
Nicolas Le Roux, Yoshua Bengio
ICML
2005
IEEE
14 years 11 months ago
Healing the relevance vector machine through augmentation
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties ...
Carl Edward Rasmussen, Joaquin Quiñonero Ca...
WSDM
2012
ACM
259views Data Mining» more  WSDM 2012»
12 years 5 months ago
Learning recommender systems with adaptive regularization
Many factorization models like matrix or tensor factorization have been proposed for the important application of recommender systems. The success of such factorization models dep...
Steffen Rendle