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JMLR
2010
194views more  JMLR 2010»
14 years 4 months ago
Graphical Gaussian modelling of multivariate time series with latent variables
In time series analysis, inference about causeeffect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal struc...
Michael Eichler
94
Voted
JMLR
2010
161views more  JMLR 2010»
14 years 4 months ago
Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is the loss function of the learning...
Lin Xiao
97
Voted
JMLR
2010
129views more  JMLR 2010»
14 years 4 months ago
Expectation Truncation and the Benefits of Preselection In Training Generative Models
We show how a preselection of hidden variables can be used to efficiently train generative models with binary hidden variables. The approach is based on Expectation Maximization (...
Jörg Lücke, Julian Eggert
81
Voted
JMLR
2010
151views more  JMLR 2010»
14 years 4 months ago
Understanding the difficulty of training deep feedforward neural networks
Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with e...
Xavier Glorot, Yoshua Bengio
92
Voted
JMLR
2010
192views more  JMLR 2010»
14 years 4 months ago
Efficient Learning of Deep Boltzmann Machines
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
Ruslan Salakhutdinov, Hugo Larochelle