Elements of information theory
Elements of information theory
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
An Introduction to Variational Methods for Graphical Models
Machine Learning
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
A hierarchical approach to continuous gesture analysis for natural multi-modal interaction
Proceedings of the 14th ACM international conference on Multimodal interaction
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We present a fast algorithm for learning the parameters of the abstract hidden Markov model, a type of hierarchical activity recognition model. Learning using exact inference scales poorly as the number of levels in the hierarchy increases; therefore, an approximation is required for large models. We demonstrate that variational inference is well suited to solve this problem. Not only does this technique scale. but it also offers a natural way to leverage the context specific independence properties inherent in the model via the fixed point equations. Experiments confirm that the variational approximation significantly reduces the time necessary for learning while estimating parameter values that can be used to make reliable predictions.