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SIAMIS
2011
15 years 1 months ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Matthias W. Seeger, Hannes Nickisch
DAC
2004
ACM
16 years 7 months ago
Statistical optimization of leakage power considering process variations using dual-Vth and sizing
timing analysis tools to replace standard deterministic static timing analyzers whereas [8,27] develop approaches for the statistical estimation of leakage power considering within...
Ashish Srivastava, Dennis Sylvester, David Blaauw
IBPRIA
2009
Springer
15 years 11 months ago
Inference and Learning for Active Sensing, Experimental Design and Control
In this paper we argue that maximum expected utility is a suitable framework for modeling a broad range of decision problems arising in pattern recognition and related fields. Exa...
Hendrik Kück, Matthew Hoffman, Arnaud Doucet,...
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ATAL
2010
Springer
15 years 7 months ago
Scalable mechanism design for the procurement of services with uncertain durations
In this paper, we study a service procurement problem with uncertainty as to whether service providers are capable of completing a given task within a specified deadline. This typ...
Enrico Gerding, Sebastian Stein, Kate Larson, Alex...
GECCO
2008
Springer
239views Optimization» more  GECCO 2008»
15 years 7 months ago
Multiobjective design of operators that detect points of interest in images
In this paper, a multiobjective (MO) learning approach to image feature extraction is described, where Pareto-optimal interest point (IP) detectors are synthesized using genetic p...
Leonardo Trujillo, Gustavo Olague, Evelyne Lutton,...