In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representa...
In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention in visual perception and many other areas of science. We propose an efficient al...
In this paper, we introduce the use of nonlinear dimension reduction for the analysis of functional neuroimaging datasets. Using a Laplacian Embedding approach, we show the power ...
Power is considered to be the major limiter to the design of more faster and complex processors in the near future. In order to address this challenge, a combination of process, c...
David Duarte, Narayanan Vijaykrishnan, Mary Jane I...
Abstract. We investigate several alternate characterizations of pseudorandom functions (PRFs) and pseudorandom permutations (PRPs) in a concrete security setting. By analyzing the ...