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ICML
2010
IEEE
13 years 10 months ago
Local Minima Embedding
Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
Minyoung Kim, Fernando De la Torre
BMCBI
2006
154views more  BMCBI 2006»
13 years 9 months ago
Analysis with respect to instrumental variables for the exploration of microarray data structures
Background: Evaluating the importance of the different sources of variations is essential in microarray data experiments. Complex experimental designs generally include various fa...
Florent Baty, Michaël Facompré, Jan Wi...
ICCD
2005
IEEE
109views Hardware» more  ICCD 2005»
14 years 5 months ago
Reducing the Energy of Speculative Instruction Schedulers
Energy dissipation from the issue queue and register file constitutes a large portion of the overall energy budget of an aggressive dynamically scheduled microprocessor. We propo...
Yongxiang Liu, Gokhan Memik, Glenn Reinman
ISCAS
2008
IEEE
111views Hardware» more  ISCAS 2008»
14 years 3 months ago
Low-power traceback MAP decoding for double-binary convolutional turbo decoder
—Convolutional turbo decoding requires large data access and consumes large memories. To reduce the size of the metrics memory, the traceback MAP decoding is introduced for doubl...
Cheng-Hung Lin, Chun-Yu Chen, An-Yeu Wu
CP
2007
Springer
14 years 3 months ago
AND/OR Multi-valued Decision Diagrams for Constraint Optimization
We propose a new top down search-based algorithm for compiling AND/OR Multi-Valued Decision Diagrams (AOMDDs), as representations of the optimal set of solutions for constraint opt...
Robert Mateescu, Radu Marinescu 0002, Rina Dechter