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» PRL: A probabilistic relational language
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ESOP
2011
Springer
14 years 9 months ago
Measure Transformer Semantics for Bayesian Machine Learning
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
Johannes Borgström, Andrew D. Gordon, Michael...
ACL
2007
15 years 7 months ago
Generating Complex Morphology for Machine Translation
We present a novel method for predicting inflected word forms for generating morphologically rich languages in machine translation. We utilize a rich set of syntactic and morphol...
Einat Minkov, Kristina Toutanova, Hisami Suzuki
CORR
2012
Springer
170views Education» more  CORR 2012»
14 years 1 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
ILP
2000
Springer
15 years 9 months ago
Bayesian Logic Programs
First-order probabilistic models are recognized as efficient frameworks to represent several realworld problems: they combine the expressive power of first-order logic, which serv...
Kristian Kersting, Luc De Raedt
ISI
2006
Springer
15 years 6 months ago
Computational Modeling and Experimental Validation of Aviation Security Procedures
Security of civil aviation has become a major concern in recent years, leading to a variety of protective measures related to airport and aircraft security to be established by re...
Uwe Glässer, Sarah Rastkar, Mona Vajihollahi