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» Towards Optimal Bayesian Algorithmic Mechanism Design
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DAC
2006
ACM
14 years 8 months ago
Systematic temperature sensor allocation and placement for microprocessors
Modern high performance processors employ advanced techniques for thermal management, which rely on accurate readings of on-die thermal sensors. As the importance of thermal effec...
Rajarshi Mukherjee, Seda Ogrenci Memik
ICTAI
2003
IEEE
14 years 25 days ago
Engineering Optimization Using a Simple Evolutionary Algorithm
This paper presents a simple ¢¤£¦¥¨§© Evolution Strategy and three simple selection criteria to solve engineering optimization problems. This approach avoids the use of a...
Efrén Mezura-Montes, Carlos A. Coello Coell...
BMCBI
2007
146views more  BMCBI 2007»
13 years 7 months ago
Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data
Background: Transcriptional modules (TM) consist of groups of co-regulated genes and transcription factors (TF) regulating their expression. Two high-throughput (HT) experimental ...
Xiangdong Liu, Walter J. Jessen, Siva Sivaganesan,...
CEC
2005
IEEE
14 years 1 months ago
Relationships between internal and external metrics in co-evolution
Co-evolutionary algorithms (CEAs) have been applied to optimization and machine learning problems with often mediocre results. One of the causes for the unfulfilled expectations i...
Elena Popovici, Kenneth A. De Jong
JMLR
2008
159views more  JMLR 2008»
13 years 7 months ago
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to ...
Andreas Krause, Ajit Paul Singh, Carlos Guestrin