We describe a neurally-inspired, unsupervised learning algorithm that builds a non-linear generative model for pairs of face images from the same individual. Individuals are then ...
Recursive graphical models usually underlie the statistical modelling concerning probabilistic expert systems based on Bayesian networks. This paper de nes a version of these mode...
We present a time–parallel technique for the fast generation of self–similar traffic which is suitable for performance studies of Asynchronous Transfer Mode (ATM) networks. Th...
Ioanis Nikolaidis, C. Anthony Cooper, Kalyan S. Pe...
We present a system for describing and solving closed queuing network models of the memory access performance of NUMA architectures. The system consists of a model description lan...
: Partially-observable Markov decision processes provide a very general model for decision-theoretic planning problems, allowing the trade-offs between various courses of actions t...