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JMLR
2010
137views more  JMLR 2010»
15 years 1 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
NPL
2011
14 years 9 months ago
A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series
The accuracy of a model to forecast a time series diminishes as the prediction horizon increases, in particular when the prediction is carried out recursively. Such decay is faster...
Pilar Gómez-Gil, Juan Manuel Ramírez...
ICML
2008
IEEE
16 years 7 months ago
A reproducing kernel Hilbert space framework for pairwise time series distances
A good distance measure for time series needs to properly incorporate the temporal structure, and should be applicable to sequences with unequal lengths. In this paper, we propose...
Zhengdong Lu, Todd K. Leen, Yonghong Huang, Deniz ...
POPL
2007
ACM
16 years 7 months ago
Locality approximation using time
Reuse distance (i.e. LRU stack distance) precisely characterizes program locality and has been a basic tool for memory system research since the 1970s. However, the high cost of m...
Xipeng Shen, Jonathan Shaw, Brian Meeker, Chen Din...
ICCAD
2006
IEEE
106views Hardware» more  ICCAD 2006»
16 years 3 months ago
Wire density driven global routing for CMP variation and timing
In this paper, we propose the first wire density driven global routing that considers CMP variation and timing. To enable CMP awareness during global routing, we propose a compac...
Minsik Cho, David Z. Pan, Hua Xiang, Ruchir Puri