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ICMLA
2008
14 years 11 months ago
Probabilistic Exploitation of the Lucas and Kanade Smoothness Constraint
The basic idea of Lucas and Kanade is to constrain the local motion measurement by assuming a constant velocity within a spatial neighborhood. We reformulate this spatial constrai...
Volker Willert, Julian Eggert, Marc Toussaint, Edg...
103
Voted
NIPS
2001
14 years 11 months ago
Latent Dirichlet Allocation
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian m...
David M. Blei, Andrew Y. Ng, Michael I. Jordan
67
Voted
NIPS
2000
14 years 11 months ago
Explaining Away in Weight Space
Explaining away has mostly been considered in terms of inference of states in belief networks. We show how it can also arise in a Bayesian context in inference about the weights g...
Peter Dayan, Sham Kakade
69
Voted
ICML
2010
IEEE
14 years 10 months ago
Mixed Membership Matrix Factorization
Discrete mixed membership modeling and continuous latent factor modeling (also known as matrix factorization) are two popular, complementary approaches to dyadic data analysis. In...
Lester W. Mackey, David Weiss, Michael I. Jordan
IVC
2006
183views more  IVC 2006»
14 years 9 months ago
Augmented tracking with incomplete observation and probabilistic reasoning
An on-line algorithm for multi-object tracking is presented for monitoring a real-world scene from a single fixed camera. Potential objects are detected with adaptive backgrounds ...
Ming Xu, Tim Ellis