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NIPS
2007
15 years 4 months ago
What makes some POMDP problems easy to approximate?
Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimension...
David Hsu, Wee Sun Lee, Nan Rong
ICML
2010
IEEE
15 years 3 months ago
Learning Fast Approximations of Sparse Coding
In Sparse Coding (SC), input vectors are reconstructed using a sparse linear combination of basis vectors. SC has become a popular method for extracting features from data. For a ...
Karol Gregor, Yann LeCun
128
Voted
AMAI
2006
Springer
15 years 2 months ago
Symmetric approximate linear programming for factored MDPs with application to constrained problems
A weakness of classical Markov decision processes (MDPs) is that they scale very poorly due to the flat state-space representation. Factored MDPs address this representational pro...
Dmitri A. Dolgov, Edmund H. Durfee
PE
2010
Springer
170views Optimization» more  PE 2010»
15 years 28 days ago
Approximating passage time distributions in queueing models by Bayesian expansion
We introduce Bayesian Expansion (BE), an approximate numerical technique for passage time distribution analysis in queueing networks. BE uses a class of Bayesian networks to appro...
Giuliano Casale
ICDE
2007
IEEE
124views Database» more  ICDE 2007»
16 years 3 months ago
On MBR Approximation of Histories for Historical Queries: Expectations and Limitations
Traditional approaches for efficiently processing historical queries, where a history is a multidimensional timeseries, employ a two step filter-and-refine scheme. In the filter s...
Reza Sherkat, Davood Rafiei