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» Approximate reduction of dynamic systems
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103
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AMC
2008
99views more  AMC 2008»
15 years 28 days 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 2 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,...
100
Voted
ICCD
2007
IEEE
322views Hardware» more  ICCD 2007»
15 years 9 months 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
113
Voted
ISQED
2007
IEEE
165views Hardware» more  ISQED 2007»
15 years 7 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
128
Voted
AUTOMATICA
2008
139views more  AUTOMATICA 2008»
15 years 28 days 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