The demand for high performance has driven acyclic computation accelerators into extensive use in modern embedded and desktop architectures. Accelerators that are ideal from a sof...
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
Subspace-based methods rely on dominant element selection from second order statistics. They have been extended to tensor processing, in particular to tensor data filtering. For t...
Background: Maximum parsimony phylogenetic tree reconstruction from genetic variation data is a fundamental problem in computational genetics with many practical applications in p...
Srinath Sridhar, Fumei Lam, Guy E. Blelloch, R. Ra...
IP packet streams consist of multiple interleaving IP flows. Statistical summaries of these streams, collected for different measurement periods, are used for characterization of ...
Edith Cohen, Nick G. Duffield, Haim Kaplan, Carste...