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» Approximate reduction of dynamic systems
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AMC
2008
99views more  AMC 2008»
15 years 4 months ago
Markov chain network training and conservation law approximations: Linking microscopic and macroscopic models for evolution
In this paper, a general framework for the analysis of a connection between the training of artificial neural networks via the dynamics of Markov chains and the approximation of c...
Roderick V. N. Melnik
NIPS
2008
15 years 5 months ago
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switc...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
ICCD
2007
IEEE
322views Hardware» more  ICCD 2007»
16 years 24 days ago
Voltage drop reduction for on-chip power delivery considering leakage current variations
In this paper, we propose a novel on-chip voltage drop reduction technique for on-chip power delivery networks of VLSI systems in the presence of variational leakage current sourc...
Jeffrey Fan, Ning Mi, Sheldon X.-D. Tan
ISQED
2007
IEEE
165views Hardware» more  ISQED 2007»
15 years 10 months ago
On-Line Adjustable Buffering for Runtime Power Reduction
We present a novel technique to exploit the power-performance tradeoff. The technique can be used stand-alone or in conjunction with dynamic voltage scaling, the mainstream techn...
Andrew B. Kahng, Sherief Reda, Puneet Sharma
AUTOMATICA
2008
139views more  AUTOMATICA 2008»
15 years 4 months ago
Structured low-rank approximation and its applications
Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equival...
Ivan Markovsky