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JMLR
2010
202views more  JMLR 2010»
14 years 10 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
173
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TNN
2010
155views Management» more  TNN 2010»
14 years 10 months ago
Incorporating the loss function into discriminative clustering of structured outputs
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations ac...
Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. ...
130
Voted
TSP
2010
14 years 10 months ago
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
145
Voted
ACL
2012
13 years 6 months ago
Automatic Event Extraction with Structured Preference Modeling
This paper presents a novel sequence labeling model based on the latent-variable semiMarkov conditional random fields for jointly extracting argument roles of events from texts. ...
Wei Lu, Dan Roth
108
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DCC
2010
IEEE
15 years 10 months ago
Advantages of Shared Data Structures for Sequences of Balanced Parentheses
We propose new data structures for navigation in sequences of balanced parentheses, a standard tool for representing compressed trees. The most striking property of our approach is...
Simon Gog, Johannes Fischer