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PKDD
2010
Springer
129views Data Mining» more  PKDD 2010»
13 years 6 months ago
Smarter Sampling in Model-Based Bayesian Reinforcement Learning
Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
Pablo Samuel Castro, Doina Precup
ISPASS
2006
IEEE
14 years 1 months ago
Comparing multinomial and k-means clustering for SimPoint
SimPoint is a technique used to pick what parts of the program’s execution to simulate in order to have a complete picture of execution. SimPoint uses data clustering algorithms...
Greg Hamerly, Erez Perelman, Brad Calder
BMCBI
2006
115views more  BMCBI 2006»
13 years 7 months ago
Detecting recombination in evolving nucleotide sequences
Background: Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. These recombination events can be obscured by subsequent resid...
Cheong Xin Chan, Robert G. Beiko, Mark A. Ragan
ICRA
2010
IEEE
147views Robotics» more  ICRA 2010»
13 years 6 months ago
Learning physically-instantiated game play through visual observation
Abstract— We present an integrated vision and robotic system that plays, and learns to play, simple physically-instantiated board games that are variants of TIC TAC TOE and HEXAP...
Andrei Barbu, Siddharth Narayanaswamy, Jeffrey Mar...
ML
2002
ACM
114views Machine Learning» more  ML 2002»
13 years 7 months ago
Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts
The execution order of a block of computer instructions on a pipelined machine can make a difference in running time by a factor of two or more. Compilers use heuristic schedulers...
Amy McGovern, J. Eliot B. Moss, Andrew G. Barto