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JMLR
2010
137views more  JMLR 2010»
13 years 3 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
CVPR
2005
IEEE
14 years 10 months ago
A Hybrid Graphical Model for Robust Feature Extraction from Video
We consider a visual scene analysis scenario where objects (e.g. people, cars) pass through the viewing field of a static camera and need to be detected and segmented from the bac...
Ali Taylan Cemgil, Wojciech Zajdel, Ben J. A. Kr&o...
JMLR
2010
125views more  JMLR 2010»
13 years 3 months ago
Variational methods for Reinforcement Learning
We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transit...
Thomas Furmston, David Barber
ECAI
2010
Springer
13 years 9 months ago
EP for Efficient Stochastic Control with Obstacles
Abstract. We address the problem of continuous stochastic optimal control in the presence of hard obstacles. Due to the non-smooth character of the obstacles, the traditional appro...
Thomas Mensink, Jakob J. Verbeek, Bert Kappen
CORR
2006
Springer
123views Education» more  CORR 2006»
13 years 8 months ago
Counting good truth assignments of random k-SAT formulae
We present a deterministic approximation algorithm to compute logarithm of the number of `good' truth assignments for a random k-satisfiability (k-SAT) formula in polynomial ...
Andrea Montanari, Devavrat Shah