Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...
Many reconfigurable architectures offer partial dynamic configurability, but current system-level tools cannot guarantee feasible implementations when exploiting this feature. We ...
Sudarshan Banerjee, Elaheh Bozorgzadeh, Nikil D. D...
—In this paper, we study the problem of stabilizing a linear time-invariant discrete-time system with information constraints in the input channels. The information constraint in...
Algorithms for learning the conditional probabilities of Bayesian networks with hidden variables typically operate within a high-dimensional search space and yield only locally op...
Abstract Existing solutions to the automated physical design problem in database systems attempt to minimize execution costs of input workloads for a given storage constraint. In t...