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CORR
2012
Springer
170views Education» more  CORR 2012»
13 years 8 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
122
Voted
IDA
2009
Springer
15 years 7 months ago
Bayesian Non-negative Matrix Factorization
Abstract. We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to ...
Mikkel N. Schmidt, Ole Winther, Lars Kai Hansen
ICA
2004
Springer
15 years 5 months ago
Blind Deconvolution Using the Relative Newton Method
We propose a relative optimization framework for quasi maximum likelihood blind deconvolution and the relative Newton method as its particular instance. Special Hessian structure a...
Alexander M. Bronstein, Michael M. Bronstein, Mich...
94
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JMLR
2010
125views more  JMLR 2010»
14 years 7 months ago
Variational methods for Reinforcement Learning
We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transit...
Thomas Furmston, David Barber
88
Voted
ISCAS
2003
IEEE
172views Hardware» more  ISCAS 2003»
15 years 5 months ago
Efficient symbol synchronization techniques using variable FIR or IIR interpolation filters
Maximum Likelihood estimation theory can be used to develop optimal timing recovery schemes for digital communication systems. Tunable digital interpolation filters are commonly ...
Martin Makundi, Timo I. Laakso