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AAAI
2011
12 years 7 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
ICRA
2010
IEEE
116views Robotics» more  ICRA 2010»
13 years 6 months ago
Parameterized maneuver learning for autonomous helicopter flight
Abstract— Many robotic control tasks involve complex dynamics that are hard to model. Hand-specifying trajectories that satisfy a system’s dynamics can be very time-consuming a...
Jie Tang, Arjun Singh, Nimbus Goehausen, Pieter Ab...
CVPR
2010
IEEE
13 years 8 months ago
Boosting for transfer learning with multiple sources
Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. Th...
Yi Yao, Gianfranco Doretto
SIGMOD
2001
ACM
136views Database» more  SIGMOD 2001»
14 years 7 months ago
Selectivity Estimation using Probabilistic Models
Estimating the result size of complex queries that involve selection on multiple attributes and the join of several relations is a difficult but fundamental task in database query...
Lise Getoor, Benjamin Taskar, Daphne Koller
AAAI
2008
13 years 10 months ago
The PELA Architecture: Integrating Planning and Learning to Improve Execution
Building architectures for autonomous rational behavior requires the integration of several AI components, such as planning, learning and execution monitoring. In most cases, the ...
Sergio Jiménez, Fernando Fernández, ...