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ML
2002
ACM
129views Machine Learning» more  ML 2002»
13 years 7 months ago
Model Selection for Small Sample Regression
Model selection is an important ingredient of many machine learning algorithms, in particular when the sample size in small, in order to strike the right trade-off between overfitt...
Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio
ISPD
2012
ACM
248views Hardware» more  ISPD 2012»
12 years 3 months ago
A fast estimation of SRAM failure rate using probability collectives
Importance sampling is a popular approach to estimate rare event failures of SRAM cells. We propose to improve importance sampling by probability collectives. First, we use “Kul...
Fang Gong, Sina Basir-Kazeruni, Lara Dolecek, Lei ...
SODA
2010
ACM
143views Algorithms» more  SODA 2010»
13 years 5 months ago
Thin Partitions: Isoperimetric Inequalities and a Sampling Algorithm for Star Shaped Bodies
Star-shaped bodies are an important nonconvex generalization of convex bodies (e.g., linear programming with violations). Here we present an efficient algorithm for sampling a giv...
Karthekeyan Chandrasekaran, Daniel Dadush, Santosh...
BMCBI
2006
113views more  BMCBI 2006»
13 years 7 months ago
GibbsST: a Gibbs sampling method for motif discovery with enhanced resistance to local optima
Background: Computational discovery of transcription factor binding sites (TFBS) is a challenging but important problem of bioinformatics. In this study, improvement of a Gibbs sa...
Kazuhito Shida
ICIP
2009
IEEE
14 years 8 months ago
Image Retargeting Using Importance Diffusion
This paper presents a simple and effective image retargeting method that preserves visually important parts while reducing unwanted distortions of an image. Our approach is based ...