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NIPS
1998
11 years 2 months ago
Inference in Multilayer Networks via Large Deviation Bounds
We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic graphs. We show that the intractability of exact inference in such networks do...
Michael J. Kearns, Lawrence K. Saul
NIPS
2008
11 years 2 months ago
Improving on Expectation Propagation
A series of corrections is developed for the fixed points of Expectation Propagation (EP), which is one of the most popular methods for approximate probabilistic inference. These ...
Manfred Opper, Ulrich Paquet, Ole Winther
ICML
2004
IEEE
12 years 2 months ago
Variational methods for the Dirichlet process
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While ...
David M. Blei, Michael I. Jordan
ECCV
2008
Springer
12 years 3 months ago
Efficiently Learning Random Fields for Stereo Vision with Sparse Message Passing
As richer models for stereo vision are constructed, there is a growing interest in learning model parameters. To estimate parameters in Markov Random Field (MRF) based stereo formu...
Jerod J. Weinman, Lam Tran, Christopher J. Pal
books