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NIPS
1998
13 years 11 months ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
ICML
2007
IEEE
14 years 10 months ago
Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation
Reinforcement learning algorithms can become unstable when combined with linear function approximation. Algorithms that minimize the mean-square Bellman error are guaranteed to co...
Chee Wee Phua, Robert Fitch
EACL
2009
ACL Anthology
14 years 10 months ago
Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings
We introduce cube summing, a technique that permits dynamic programming algorithms for summing over structures (like the forward and inside algorithms) to be extended with non-loc...
Kevin Gimpel, Noah A. Smith
WWW
2005
ACM
14 years 10 months ago
Static approximation of dynamically generated Web pages
Server-side programming is one of the key technologies that support today's WWW environment. It makes it possible to generate Web pages dynamically according to a user's...
Yasuhiko Minamide
SCCC
2003
IEEE
14 years 3 months ago
Improved Deletions in Dynamic Spatial Approximation Trees
The Dynamic Spatial Approximation Tree (dsa–tree) is a recently proposed data structure for searching in metric spaces. It has been shown that it compares favorably against alte...
Gonzalo Navarro, Nora Reyes