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JMLR
2010
192views more  JMLR 2010»
12 years 10 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
IJAR
2006
98views more  IJAR 2006»
13 years 3 months ago
A forward-backward Monte Carlo method for solving influence diagrams
Although influence diagrams are powerful tools for representing and solving complex decisionmaking problems, their evaluation may require an enormous computational effort and this...
Andrés Cano, Manuel Gómez, Seraf&iac...
NIPS
1998
13 years 5 months ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
SIGMOD
2010
ACM
211views Database» more  SIGMOD 2010»
13 years 8 months ago
ERACER: a database approach for statistical inference and data cleaning
Real-world databases often contain syntactic and semantic errors, in spite of integrity constraints and other safety measures incorporated into modern DBMSs. We present ERACER, an...
Chris Mayfield, Jennifer Neville, Sunil Prabhakar
AIME
2007
Springer
13 years 9 months ago
Inference in the Promedas Medical Expert System
Abstract. In the current paper, the Promedas model for internal medicine, developed by our team, is introduced. The model is based on up-todate medical knowledge and consists of ap...
Bastian Wemmenhove, Joris M. Mooij, Wim Wiegerinck...
CVPR
2010
IEEE
14 years 7 hour ago
Beyond Trees: MRF Inference via Outer-Planar Decomposition
Maximum a posteriori (MAP) inference in Markov Random Fields (MRFs) is an NP-hard problem, and thus research has focussed on either finding efficiently solvable subclasses (e.g. t...
Dhruv Batra, Andrew Gallagher, Devi Parikh, Tsuhan...