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JMLR
2010
143views more  JMLR 2010»
11 years 1 months ago
Incremental Sigmoid Belief Networks for Grammar Learning
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...
James Henderson, Ivan Titov
SAFECOMP
2010
Springer
11 years 5 months ago
Deriving Safety Cases for Hierarchical Structure in Model-Based Development
Abstract. Model-based development and automated code generation are increasingly used for actual production code, in particular in mathematical and engineering domains. However, si...
Nurlida Basir, Ewen Denney, Bernd Fischer 0002
NN
2002
Springer
136views Neural Networks» more  NN 2002»
11 years 6 months ago
Bayesian model search for mixture models based on optimizing variational bounds
When learning a mixture model, we suffer from the local optima and model structure determination problems. In this paper, we present a method for simultaneously solving these prob...
Naonori Ueda, Zoubin Ghahramani
JMLR
2007
137views more  JMLR 2007»
11 years 6 months ago
Building Blocks for Variational Bayesian Learning of Latent Variable Models
We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, a...
Tapani Raiko, Harri Valpola, Markus Harva, Juha Ka...
JCP
2007
100views more  JCP 2007»
11 years 6 months ago
Modelling Internet End-to-End Loss Behaviors: A Congestion Control Perspective
— This paper proposes a new approach to modelling and controlling Internet end-to-end loss behaviours. Rather than select the model structure from the loss observations as being ...
Vinh Bui, Weiping Zhu, Ruhul A. Sarker
NIPS
1998
11 years 8 months ago
SMEM Algorithm for Mixture Models
When learning a mixture model, we suffer from the local optima and model structure determination problems. In this paper, we present a method for simultaneously solving these prob...
Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Ge...
ICMCS
2008
IEEE
207views Multimedia» more  ICMCS 2008»
12 years 1 months ago
Structure learning in a Bayesian network-based video indexing framework
Several stochastic models provide an effective framework to identify the temporal structure of audiovisual data. Most of them need as input a first video structure, i.e. connecti...
Siwar Baghdadi, Guillaume Gravier, Claire-Hé...
ICML
2007
IEEE
12 years 7 months ago
Incremental Bayesian networks for structure prediction
We propose a class of graphical models appropriate for structure prediction problems where the model structure is a function of the output structure. Incremental Sigmoid Belief Ne...
Ivan Titov, James Henderson
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