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» Learning from Highly Structured Data by Decomposition
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MICRO
1999
IEEE
123views Hardware» more  MICRO 1999»
15 years 8 months ago
Improving Branch Predictors by Correlating on Data Values
Branch predictors typically use combinations of branch PC bits and branch histories to make predictions. Recent improvements in branch predictors have come from reducing the effec...
Timothy H. Heil, Zak Smith, James E. Smith
IPMI
2005
Springer
16 years 5 months ago
Segmenting and Tracking the Left Ventricle by Learning the Dynamics in Cardiac Images
Having accurate left ventricle (LV) segmentations across a cardiac cycle provides useful quantitative (e.g. ejection fraction) and qualitative information for diagnosis of certain ...
Alan S. Willsky, Godtfred Holmvang, Müjdat &C...
ICDM
2009
IEEE
137views Data Mining» more  ICDM 2009»
15 years 11 months ago
Set-Based Boosting for Instance-Level Transfer
—The success of transfer to improve learning on a target task is highly dependent on the selected source data. Instance-based transfer methods reuse data from the source tasks to...
Eric Eaton, Marie desJardins
CVPR
2006
IEEE
16 years 6 months ago
Dimensionality Reduction by Learning an Invariant Mapping
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that "similar" points in input space are mapped to ne...
Raia Hadsell, Sumit Chopra, Yann LeCun
SENSYS
2006
ACM
15 years 10 months ago
Scalable data aggregation for dynamic events in sensor networks
Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Mor...
Kai-Wei Fan, Sha Liu, Prasun Sinha