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» Learning the Structure of Linear Latent Variable Models
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AAAI
2008
13 years 10 months ago
Latent Tree Models and Approximate Inference in Bayesian Networks
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
Yi Wang, Nevin Lianwen Zhang, Tao Chen
UAI
2000
13 years 9 months ago
Tractable Bayesian Learning of Tree Belief Networks
In this paper we present decomposable priors, a family of priors over structure and parameters of tree belief nets for which Bayesian learning with complete observations is tracta...
Marina Meila, Tommi Jaakkola
ICML
2010
IEEE
13 years 8 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
ICML
2006
IEEE
14 years 8 months ago
Topic modeling: beyond bag-of-words
Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assu...
Hanna M. Wallach
KDD
2008
ACM
186views Data Mining» more  KDD 2008»
14 years 8 months ago
Cut-and-stitch: efficient parallel learning of linear dynamical systems on smps
Multi-core processors with ever increasing number of cores per chip are becoming prevalent in modern parallel computing. Our goal is to make use of the multi-core as well as multi...
Lei Li, Wenjie Fu, Fan Guo, Todd C. Mowry, Christo...