In this work, we design two-party and multiparty protocols for evaluating multivariate polynomials at participants' inputs with security against a malicious adversary who may ...
Deep autoencoder networks have successfully been applied in unsupervised dimension reduction. The autoencoder has a "bottleneck" middle layer of only a few hidden units, ...
Abstract-- In this paper we present a method for automatically generating optimal robot trajectories satisfying high level mission specifications. The motion of the robot in the en...
Stephen L. Smith, Jana Tumova, Calin Belta, Daniel...
Kernel Principal Component Analysis extends linear PCA from a Euclidean space to any reproducing kernel Hilbert space. Robustness issues for Kernel PCA are studied. The sensitivit...
We consider the unrelated parallel machines scheduling problem where jobs have earliness and tardiness penalties and a common due date. We formulate this problem and some of its v...