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» Causal inference using the algorithmic Markov condition
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AAAI
2006
14 years 11 months ago
Fast Hierarchical Goal Schema Recognition
We present our work on using statistical, corpus-based machine learning techniques to simultaneously recognize an agent's current goal schemas at various levels of a hierarch...
Nate Blaylock, James F. Allen
CVPR
2010
IEEE
1790views Computer Vision» more  CVPR 2010»
15 years 6 months ago
Data Driven Mean-Shift Belief Propagation For non-Gaussian MRFs
We introduce a novel data-driven mean-shift belief propagation (DDMSBP) method for non-Gaussian MRFs, which often arise in computer vision applications. With the aid of scale sp...
Minwoo Park, S. Kashyap, R. Collins, and Y. Liu
JAIR
2006
179views more  JAIR 2006»
14 years 9 months ago
The Fast Downward Planning System
Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, ...
Malte Helmert
ICRA
2009
IEEE
218views Robotics» more  ICRA 2009»
14 years 7 months ago
Automatically and efficiently inferring the hierarchical structure of visual maps
In Simultaneous Localisation and Mapping (SLAM), it is well known that probabilistic filtering approaches which aim to estimate the robot and map state sequentially suffer from poo...
Margarita Chli, Andrew J. Davison
JCST
2010
139views more  JCST 2010»
14 years 8 months ago
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
Dilan Görür, Carl Edward Rasmussen