We study how the dependence of a simulation output on an uncertain parameter can be determined, when simulations are computationally expensive and so can only be run for very few p...
: PSO, like many stochastic search methods, is very sensitive to efficient parameter setting such that modifying a single parameter may cause a considerable change in the result. I...
We study the efficient evaluation of top-k queries over data items, where the score of each item is dynamically computed by applying an item-specific function whose parameter valu...
Lin Guo, Sihem Amer-Yahia, Raghu Ramakrishnan, Jay...
Image restoration from degraded images lies at the foundation of image processing, pattern recognition, and computer vision, so it has been extensively studied. A large number of ...
When aligning biological sequences, the choice of scoring scheme is critical. Even small changes in gap penalties, for example, can yield radically different alignments. A rigorous...
– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...
Solving design and analysis problems in physical worlds requires the representatio n of large amounts of knowledge. Recently, there has been much interest in explicitly making ass...
Sanjaya Addanki, Roberto Cremonini, J. Scott Penbe...
We present components of a system which uses statistical, corpus-based machine learning techniques to perform instantiated goal recognition -- recognition of both a goal schema an...
Biologically focused, agent-based models need many parameters in order to simulate system dynamics. It is often essential to explore the consequences of many parameter vectors bef...