An efficient training method for block-diagonal recurrent neural networks is proposed. The method modifies the RPROP algorithm, originally developed for static models, in order to...
Paris A. Mastorocostas, Dimitris N. Varsamis, Cons...
Abstract A lateral-inhibition type neural field model with restricted connections is presented here and represents an experimental extension of the Continuum Neural Field Theory (C...
We present a novel dynamic network interdiction model that accounts for interactions between an interdictor deploying resources on arcs in a digraph and an evader traversing the ne...
An innovative technique to model and simulate partial and dynamic reconfigurable processors is presented in this paper. The basis for development is a SystemC kernel modified for ...
Dynamic group Diffie-Hellman protocols for Authenticated Key Exchange (AKE) are designed to work in a scenario in which the group membership is not known in advance but where parti...
Emmanuel Bresson, Olivier Chevassut, David Pointch...