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» Localizing Search in Reinforcement Learning
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ICML
2005
IEEE
14 years 10 months ago
Q-learning of sequential attention for visual object recognition from informative local descriptors
This work provides a framework for learning sequential attention in real-world visual object recognition, using an architecture of three processing stages. The first stage rejects...
Lucas Paletta, Gerald Fritz, Christin Seifert
COR
2010
146views more  COR 2010»
13 years 10 months ago
A search space "cartography" for guiding graph coloring heuristics
We present a search space analysis and its application in improving local search algorithms for the graph coloring problem. Using a classical distance measure between colorings, w...
Daniel Cosmin Porumbel, Jin-Kao Hao, Pascale Kuntz
ATAL
2006
Springer
14 years 1 months ago
Scalable and reliable data delivery in mobile ad hoc sensor networks
This paper studies scalable data delivery algorithms in mobile ad hoc sensor networks with node and link failures. Many algorithms have been developed for data delivery and fusion...
Bin Yu, Paul Scerri, Katia P. Sycara, Yang Xu, Mic...
NIPS
2008
13 years 11 months ago
Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
John W. Roberts, Russ Tedrake
UAI
2001
13 years 11 months ago
Improved learning of Bayesian networks
The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Baye...
Tomás Kocka, Robert Castelo