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» On the Use of Restrictions for Learning Bayesian Networks
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NECO
2010
136views more  NECO 2010»
13 years 5 months ago
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines
To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicat...
Roland Memisevic, Geoffrey E. Hinton
AAAI
2008
13 years 9 months ago
Factored Models for Probabilistic Modal Logic
Modal logic represents knowledge that agents have about other agents' knowledge. Probabilistic modal logic further captures probabilistic beliefs about probabilistic beliefs....
Afsaneh Shirazi, Eyal Amir
PASTE
2010
ACM
14 years 15 days ago
Learning universal probabilistic models for fault localization
Recently there has been significant interest in employing probabilistic techniques for fault localization. Using dynamic dependence information for multiple passing runs, learnin...
Min Feng, Rajiv Gupta
CF
2007
ACM
13 years 11 months ago
Fast compiler optimisation evaluation using code-feature based performance prediction
Performance tuning is an important and time consuming task which may have to be repeated for each new application and platform. Although iterative optimisation can automate this p...
Christophe Dubach, John Cavazos, Björn Franke...
OWLED
2007
13 years 8 months ago
Ontology-Based Management of the Telehealth Smart Home, Dedicated to Elderly in Loss of Cognitive Autonomy
Taking care of an elderly in loss of cognitive autonomy is a challenging task. Artificial agents, such as the Telehealth Smart Home (TSH) system can facilitate that task. However,...
Fatiha Latfi, Bernard Lefebvre, Céline Desc...