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IJCAI
2007
13 years 9 months ago
Collapsed Variational Dirichlet Process Mixture Models
Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of ...
Kenichi Kurihara, Max Welling, Yee Whye Teh
GLVLSI
2008
IEEE
129views VLSI» more  GLVLSI 2008»
14 years 2 months ago
Variational capacitance modeling using orthogonal polynomial method
In this paper, we propose a novel statistical capacitance extraction method for interconnects considering process variations. The new method, called statCap, is based on the spect...
Jian Cui, Gengsheng Chen, Ruijing Shen, Sheldon X....
DATE
2009
IEEE
129views Hardware» more  DATE 2009»
14 years 2 months ago
Improved performance and variation modelling for hierarchical-based optimisation of analogue integrated circuits
A new approach in hierarchical optimisation is presented which is capable of optimising both the performance and yield of an analogue design. Performance and yield trade offs are ...
Sawal Ali, Li Ke, Reuben Wilcock, Peter Wilson
ISQED
2007
IEEE
151views Hardware» more  ISQED 2007»
14 years 2 months ago
Gate Level Statistical Simulation Based on Parameterized Models for Process and Signal Variations
We propose gate level statistical simulation to bridge the gap between the most accurate Monte Carlo SPICE simulation and the most efficient circuit level statistical static timi...
Bao Liu
PR
2010
129views more  PR 2010»
13 years 6 months ago
Parsimonious reduction of Gaussian mixture models with a variational-Bayes approach
Aggregating statistical representations of classes is an important task for current trends in scaling up learning and recognition, or for addressing them in distributed infrastruc...
Pierrick Bruneau, Marc Gelgon, Fabien Picarougne