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AAAI
2008
15 years 9 months ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
AIPS
2008
15 years 9 months ago
Useless Actions Are Useful
Planning as heuristic search is a powerful approach to solving domain independent planning problems. In recent years, various successful heuristics and planners like FF, LPG, FAST...
Martin Wehrle, Sebastian Kupferschmid, Andreas Pod...
VCIP
2001
110views Communications» more  VCIP 2001»
15 years 8 months ago
Lossless and near-lossless image compression with successive refinement
We present a technique that provides progressive transmission and near-lossless compression in one single framework. The proposed technique produces a bitstream that results in pr...
Ismail Avcibas, Nasir D. Memon, Bülent Sankur...
SODA
2004
ACM
124views Algorithms» more  SODA 2004»
15 years 8 months ago
On contract-and-refine transformations between phylogenetic trees
The inference of evolutionary trees using approaches which attempt to solve the maximum parsimony (MP) and maximum likelihood (ML) optimization problems is a standard part of much...
Ganeshkumar Ganapathy, Vijaya Ramachandran, Tandy ...
ICONIP
1998
15 years 8 months ago
Computing Iterative Roots with Neural Networks
Many real processes are composed of a n-fold repetition of some simpler process. If the whole process can be modelled with a neural network, we present a method to derive a model ...
Lars Kindermann