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JAIR
2010
145views more  JAIR 2010»
13 years 6 months ago
Planning with Noisy Probabilistic Relational Rules
Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually in...
Tobias Lang, Marc Toussaint
AR
2007
105views more  AR 2007»
13 years 7 months ago
Reinforcement learning of a continuous motor sequence with hidden states
—Reinforcement learning is the scheme for unsupervised learning in which robots are expected to acquire behavior skills through self-explorations based on reward signals. There a...
Hiroaki Arie, Tetsuya Ogata, Jun Tani, Shigeki Sug...
AI
2005
Springer
13 years 7 months ago
Learning to talk about events from narrated video in a construction grammar framework
The current research presents a system that learns to understand object names, spatial relation terms and event descriptions from observing narrated action sequences. The system e...
Peter Ford Dominey, Jean-David Boucher
CVPR
2005
IEEE
14 years 9 months ago
Learning Spatiotemporal T-Junctions for Occlusion Detection
The goal of motion segmentation and layer extraction can be viewed as the detection and localization of occluding surfaces. A feature that has been shown to be a particularly stro...
Nicholas Apostoloff, Andrew W. Fitzgibbon
ICIP
2009
IEEE
13 years 5 months ago
Classified patch learning for spatially scalable video coding
This paper proposes an advanced spatially scalable video coding approach that exploits the inter layer correlation between different resolution layers by classified patch learning...
Xiaoyan Sun, Feng Wu