Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
Background: Evaluating the importance of the different sources of variations is essential in microarray data experiments. Complex experimental designs generally include various fa...
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...
—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...
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