We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
There is increasing evidence to suggest that the neocortex of the mammalian brain does not consist of a collection of specialised and dedicated cortical architectures, but instead ...
John Thornton, Torbjorn Gustafsson, Michael Blumen...
We introduce a new task-independent framework to model top-down overt visual attention based on graphical models for probabilistic inference and reasoning. We describe a Dynamic B...
This paper presents a framework for provisioning application and channel dependent quality of service in wireless networks. The framework is based on three di erent adaptation mec...
Javier Gomez, Andrew T. Campbell, Hiroyuki Morikaw...
We propose Laplace max-margin Markov networks (LapM3 N), and a general class of Bayesian M3 N (BM3 N) of which the LapM3 N is a special case with sparse structural bias, for robus...