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ICANNGA
2007
Springer
105views Algorithms» more  ICANNGA 2007»
14 years 1 months ago
Reinforcement Learning in Fine Time Discretization
Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet th...
Pawel Wawrzynski
NIPS
1998
13 years 9 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
ISCA
2008
IEEE
150views Hardware» more  ISCA 2008»
14 years 2 months ago
Fetch-Criticality Reduction through Control Independence
Architectures that exploit control independence (CI) promise to remove in-order fetch bottlenecks, like branch mispredicts, instruction-cache misses and fetch unit stalls, from th...
Mayank Agarwal, Nitin Navale, Kshitiz Malik, Matth...
CHI
1995
ACM
13 years 11 months ago
Belvedere: stimulating students' critical discussion
We describe “Belvedere,” a system to support students engaged in critical discussion of science and public policy issues. The design is intended to address cognitive and metac...
Massimo Paolucci, Daniel D. Suthers, Arlene Weiner
CVPR
2009
IEEE
13 years 11 months ago
CHoG: Compressed histogram of gradients A low bit-rate feature descriptor
Establishing visual correspondences is an essential component of many computer vision problems, and is often done with robust, local feature-descriptors. Transmission and storage ...
Vijay Chandrasekhar, Gabriel Takacs, David M. Chen...