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AAAI
2008
14 years 5 days ago
A General Method for Reducing the Complexity of Relational Inference and its Application to MCMC
Many real-world problems are characterized by complex relational structure, which can be succinctly represented in firstorder logic. However, many relational inference algorithms ...
Hoifung Poon, Pedro Domingos, Marc Sumner
AAAI
2008
14 years 5 days 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
BMCBI
2010
229views more  BMCBI 2010»
13 years 10 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
ECCV
2010
Springer
14 years 3 months ago
Stacked Hierarchical Labeling
In this work we propose a hierarchical approach for labeling semantic objects and regions in scenes. Our approach is reminiscent of early vision literature in that we use a decompo...
CORR
2011
Springer
199views Education» more  CORR 2011»
13 years 4 months ago
From Machine Learning to Machine Reasoning
A plausible definition of "reasoning" could be "algebraically manipulating previously acquired knowledge in order to answer a new question". This definition co...
Léon Bottou