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TSMC
2002
98views more  TSMC 2002»
13 years 7 months ago
The STAR automaton: expediency and optimality properties
Abstract--We present the STack ARchitecture (STAR) automaton. It is a fixed structure, multiaction, reward-penalty learning automaton, characterized by a star-shaped state transiti...
Anastasios A. Economides, Athanasios Kehagias
IJPP
2011
115views more  IJPP 2011»
12 years 11 months ago
Milepost GCC: Machine Learning Enabled Self-tuning Compiler
Tuning compiler optimizations for rapidly evolving hardware makes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization i...
Grigori Fursin, Yuriy Kashnikov, Abdul Wahid Memon...
MMB
2012
Springer
259views Communications» more  MMB 2012»
12 years 3 months ago
Boosting Design Space Explorations with Existing or Automatically Learned Knowledge
Abstract. During development, processor architectures can be tuned and configured by many different parameters. For benchmarking, automatic design space explorations (DSEs) with h...
Ralf Jahr, Horia Calborean, Lucian Vintan, Theo Un...
GECCO
2009
Springer
194views Optimization» more  GECCO 2009»
14 years 2 months ago
Combining evolution strategy and gradient descent method for discriminative learning of bayesian classifiers
The optimization method is one of key issues in discriminative learning of pattern classifiers. This paper proposes a hybrid approach of the Covariance Matrix Adaptation Evolution...
Xuefeng Chen, Xiabi Liu, Yunde Jia
ICML
2007
IEEE
14 years 8 months ago
Percentile optimization in uncertain Markov decision processes with application to efficient exploration
Markov decision processes are an effective tool in modeling decision-making in uncertain dynamic environments. Since the parameters of these models are typically estimated from da...
Erick Delage, Shie Mannor