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» Approximate algorithms for neural-Bayesian approaches
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NIPS
2007
15 years 7 months ago
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods
Learning in real-world domains often requires to deal with continuous state and action spaces. Although many solutions have been proposed to apply Reinforcement Learning algorithm...
Alessandro Lazaric, Marcello Restelli, Andrea Bona...
MONET
2006
107views more  MONET 2006»
15 years 6 months ago
Utilization and fairness in spectrum assignment for opportunistic spectrum access
The Open Spectrum approach to spectrum access can achieve near-optimal utilization by allowing devices to sense and utilize available spectrum opportunistically. However, a naive d...
Chunyi Peng, Haitao Zheng, Ben Y. Zhao
AUTOMATICA
2005
86views more  AUTOMATICA 2005»
15 years 6 months ago
Sensitivity shaping with degree constraint by nonlinear least-squares optimization
This paper presents a new approach to shaping of the frequency response of the sensitivity function. In this approach, a desired frequency response is assumed to be specified at a...
Ryozo Nagamune, Anders Blomqvist
AAAI
2011
14 years 6 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
CVPR
2008
IEEE
16 years 8 months ago
Skeletal graphs for efficient structure from motion
We address the problem of efficient structure from motion for large, unordered, highly redundant, and irregularly sampled photo collections, such as those found on Internet photo-...
Noah Snavely, Steven M. Seitz, Richard Szeliski