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» Learning Markov Logic Networks Using Structural Motifs
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ICML
2006
IEEE
16 years 8 months ago
Discriminative unsupervised learning of structured predictors
We present a new unsupervised algorithm for training structured predictors that is discriminative, convex, and avoids the use of EM. The idea is to formulate an unsupervised versi...
Linli Xu, Dana F. Wilkinson, Finnegan Southey, Dal...
209
Voted
PERCOM
2003
ACM
16 years 21 days ago
Recognition of Human Activity through Hierarchical Stochastic Learning
Seeking to extend the functional capability of the elderly, we explore the use of probabilistic methods to learn and recognise human activity in order to provide monitoring suppor...
Sebastian Lühr, Hung Hai Bui, Svetha Venkates...
203
Voted
GECCO
2004
Springer
142views Optimization» more  GECCO 2004»
16 years 24 days ago
Improving MACS Thanks to a Comparison with 2TBNs
Abstract. Factored Markov Decision Processes is the theoretical framework underlying multi-step Learning Classifier Systems research. This framework is mostly used in the context ...
Olivier Sigaud, Thierry Gourdin, Pierre-Henri Wuil...
237
Voted
JMLR
2010
202views more  JMLR 2010»
15 years 2 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
239
Voted
CVPR
2011
IEEE
15 years 3 months ago
Multi-agent event recognition in structured scenarios
We present a framework for the automatic recognition of complex multi-agent events in settings where structure is imposed by rules that agents must follow while performing activit...
Vlad Morariu, Larry Davis