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TSP
2008
103views more  TSP 2008»
13 years 7 months ago
Low-Rank Variance Approximation in GMRF Models: Single and Multiscale Approaches
Abstract--We present a versatile framework for tractable computation of approximate variances in large-scale Gaussian Markov random field estimation problems. In addition to its ef...
Dmitry M. Malioutov, Jason K. Johnson, Myung Jin C...
CORR
2010
Springer
127views Education» more  CORR 2010»
13 years 6 months ago
Learning Networks of Stochastic Differential Equations
We consider linear models for stochastic dynamics. To any such model can be associated a network (namely a directed graph) describing which degrees of freedom interact under the d...
José Bento, Morteza Ibrahimi, Andrea Montan...
ICCAD
2009
IEEE
117views Hardware» more  ICCAD 2009»
13 years 5 months ago
Binning optimization based on SSTA for transparently-latched circuits
With increasing process variation, binning has become an important technique to improve the values of fabricated chips, especially in high performance microprocessors where transpa...
Min Gong, Hai Zhou, Jun Tao, Xuan Zeng
PE
2011
Springer
167views Optimization» more  PE 2011»
13 years 2 months ago
Passage-time computation and aggregation strategies for large semi-Markov processes
High-level semi-Markov modelling paradigms such as semi-Markov stochastic Petri nets and process algebras are used to capture realistic performance models of computer and communic...
Marcel C. Guenther, Nicholas J. Dingle, Jeremy T. ...
ASPLOS
2011
ACM
12 years 11 months ago
NV-Heaps: making persistent objects fast and safe with next-generation, non-volatile memories
nt, user-defined objects present an attractive abstraction for working with non-volatile program state. However, the slow speed of persistent storage (i.e., disk) has restricted ...
Joel Coburn, Adrian M. Caulfield, Ameen Akel, Laur...