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AAAI
1998
15 years 8 months ago
Applying Online Search Techniques to Continuous-State Reinforcement Learning
In this paper, we describe methods for e ciently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boo...
Scott Davies, Andrew Y. Ng, Andrew W. Moore
AAAI
1997
15 years 8 months ago
Incremental Methods for Computing Bounds in Partially Observable Markov Decision Processes
Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
Milos Hauskrecht
NIPS
1998
15 years 8 months ago
Gradient Descent for General Reinforcement Learning
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
Leemon C. Baird III, Andrew W. Moore
PAMI
2008
181views more  PAMI 2008»
15 years 6 months ago
Automatic Estimation and Removal of Noise from a Single Image
Image denoising algorithms often assume an additive white Gaussian noise (AWGN) process that is independent of the actual RGB values. Such approaches cannot effectively remove colo...
Ce Liu, Richard Szeliski, Sing Bing Kang, C. Lawre...
AMC
2005
138views more  AMC 2005»
15 years 6 months ago
Optimal centers in branch-and-prune algorithms for univariate global optimization
We present an interval branch-and-prune algorithm for computing verified enclosures for the global minimum and all global minimizers of univariate functions subject to bound const...
D. G. Sotiropoulos, T. N. Grapsa